cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5575336
title: Monsoon low-pressure-system tracks over South Asia (1979-2019)

description: Monsoon LPS tracks over South Asia, computed using ERA-Interim reanalysis data.<br> Tracking algorithm described in Hunt and Fletcher (2018) [doi:10.1007/s00382-019-04744-x] and Hunt <em>et al.</em> (2016) [doi:10.1175/MWR-D-15-0138.1].<br> <br> Description of fields:<br> <em>point_id</em>: unique integer identifier for each point<br> <em>time</em>: string with format DD/MM/YY HH:MM, denoting time of detected track point<br> <em>lon</em>: longitude of detected track point<br> <em>lat</em>: latitude of detected track point<br> <em>track_id</em>: unique integer identifier for each track (constituting a group of points)<br> <em>vort</em>: relative vorticity at 850 hPa for the given point (units: s<sup>-1</sup>)<br> <em>circulation</em>: vorticity integrated over the blob of positive vorticity containing the track point (units: arb)<br> <em>eccentricity</em>: eccentricity of the vorticity blob containing the track point<br> <em>category</em>: integer from 0-6 denoting the category of the LPS at the given point, approximately matching IMD criteria. 0: low-pressure area, 1: deep low-pressure area, 2: depression, 3: deep depression, 4: cyclonic storm, 5: severe cyclonic storm, 6: very severe cyclonic storm (and above).
description: {"references": ["The relationship between Indian monsoon rainfall and low-pressure systems. KMR Hunt, JK Fletcher - Climate Dynamics, 2019", "On the structure and dynamics of Indian monsoon depressions KMR Hunt, AG Turner, PM Inness, DE Parker, RC Levine - Monthly Weather Review, 2016"]}
DOI: 10.5281/zenodo.7322874
title: Frequency Comb from a Single Driven Nonlinear Nanomechanical Mode

description: Data and analysis code for figures in "Frequency Comb from a Single Driven Nonlinear Nanomechanical Mode".
DOI: 10.6084/m9.figshare.13185410.v2
title: CNRDv1.0: the China natural runoff dataset version 1.0

description: The quality-controlled gridded naturalized runoff dataset, the China natural runoff dataset version 1.0 (CNRD v1.0), is reconstructed by using a comprehensive model parameter uncertainty analysis framework within a land-surface model. CNRD v1.0 provides daily, monthly and annual 0.25° runoff estimates for the period 1961–2018 over China. CNRD v1.0 was generated using the Variable Infiltration Capacity model with a comprehensive parameter uncertainty analysis framework incorporating parameter sensitivity analysis, optimization and regionalization based on 200 natural or near-natural gauge catchments.<br>Global attributes:<br><br>Variables = Total runoff (qtot)<br>Data Types = Histrorical reconstruction<br>Date format = NetCDF4Temporal Range = 1961-01-01 to 2018-12-31<br>Spatial extent = 18.6250° N ~53.6250° N, 73.375° E ~135.1250° EUnits = mmMissing value = -9999<br>_FillValue = -9999<br>Publications:<br><br>1. Gou, J. J., Miao, C. Y., Duan, Q. Y., Tang, Q. H., Di, Z. H., Liao, W. H., Wu, J. W., & Zhou, R. (2020). Sensitivity analysis-based automatic parameter calibration of the variable infiltration capacity (VIC) model for streamflow simulations over China. Water Resources Research, 56, https://doi.org/10.1029/2019wr025968;<br>2. Gou, J. J., Miao, C. Y., Samaniego, L., Xiao, M., Wu, J. W. & Guo, X. Y. (2020). CNRD v1.0: a high-quality natural runoff dataset for hydrological and climate studies in China. Bulletin of the American Meteorological Society (Under Review).<br>
DOI: 10.6084/m9.figshare.20755261.v3
title: Unraveling the Critical Geomagnetic Conditions in Controlling the Upper Limit of Electron Fluxes in the Earth’s Earth's Outer Radiation Belt

description: This is the dataset used to produce figures for the paper under review with the title of "Unraveling the Critical Geomagnetic Conditions in Controlling the Upper Limit of Electron Fluxes in the Earth’s Earth's Outer Radiation Belt ".
DOI: 10.17189/1520311
title: ODYSSEY THEMIS IR RDR V1.0
DOI: 10.25318/1310078801-eng
title: Chronic conditions among seniors aged 65 and older, Canadian Health Survey on Seniors

description: Chronic conditions among seniors aged 65 and older, Canadian Health Survey on Seniors, by age group and sex, Canada (excluding territories) and provinces.
DOI: 10.34681/jd5d-y494
title: National Child Abuse and Neglect Data System, Detailed Case Data Component (NCANDS DCDC), 1995-1997

description: The National Child Abuse and Neglect Data System (NCANDS) is a voluntary national data collection and analysis system created in response to the requirements of the Child Abuse Prevention and Treatment Act. The NCANDS is implemented by Walter R. McDonald and Associates, Inc. and consists of two components. The Summary Data Component (SDC) is a compilation of key aggregate child abuse and neglect statistics from all States, including data on reports, investigations, victims, and perpetrators. The Detailed Case Data Component (DCDC) is a compilation of case-level information from child protective services agencies on children who are the subjects of reports alleging child maltreatment. All fifty States and the District of Columbia participate in the SDC, however only fifteen States provided data for the DCDC in 1995. The DCDC participating states were Delaware, Florida, Illinois, Kentucky, Louisiana, Massachusetts, Missouri, North Carolina, New Jersey, Pennsylvania, South Carolina, Texas, Utah, Vermont, and Washington. A version of the 1995 NCANDS DCDC is available to the research community. The files that are available consist of selected and derived variables from the Fiscal Year 1995 NCANDS DCDC records from the fifteen DCDC participating states. These files are referred to as the NCANDS DCDC 1995 (1995 NCANDS). The 1995 NCANDS consists of 30 data files - two from each participating state. One of the two files per state contains fourteen variables and includes information about all report/child pairs of alleged maltreatment. The second file for each state contains 62 variables and includes information about all report/child pairs with substantiated or indicated maltreatment. Information is included about the types of maltreatment, the support services provided to the family, as well as caretaker, child, and family problems. Information about perpetrators of maltreatment is not included in the files. Child fatalities are not included in the data files. While the 1995 NCANDS DCDC data are not a statistically representative sample of maltreated children in the United States, they show distributions similar to those found in studies using national data. Furthermore, they provide an important opportunity to answer many questions regarding child maltreatment cases that are known to Child Protective Services.
DOI: 10.5281/zenodo.3979097
title: Replication package of "Good Things Come In Threes: Improving Search-based Crash Reproduction With Helper Objectives"

description: The replication package for the study about using new helper objectives (MOHO) for crash reproduction. This study has been accepted at ASE 2020. Abstract: Evolutionary intelligence approaches have been successfully applied to assist developers during debugging by generating a test case reproducing reported crashes. These approaches use a single fitness function called <em>Crash Distance</em> to guide the search process toward reproducing a target crash. Despite the reported achievements, these approaches do not always successfully reproduce some crashes due to a lack of test diversity (premature convergence). In this study, we introduce a new approach, called <em>MO-HO</em>, that addresses this issue via multi-objectivization. In particular, we introduce two new Helper-Objectives for crash reproduction, namely <em>test length</em> (to minimize) and <em>method sequence diversity</em> (to maximize), in addition to <em>Crash Distance</em>. We assessed <em>MO-HO</em> using five multi-objective evolutionary algorithms (NSGA-II, SPEA2, PESA-II, MOEA/D, FEMO) on 124 hard-to-reproduce crashes stemming from open-source projects. Our results indicate that SPEA2 is the best-performing multi-objective algorithm for <em>MO-HO</em>. We evaluated this best-performing algorithm for <em>MO-HO</em> against the state-of-the-art: single-objective approach (Single-Objective Search) and decomposition-based multi-objectivization approach (<em>De-MO</em>). Our results show that <em>MO-HO</em> reproduces five crashes that cannot be reproduced by the current state-of-the-art. Besides, <em>MO-HO</em> improves the effectiveness (+10% and +8% in reproduction ratio) and the efficiency in 34.6% and 36% of crashes (i.e., significantly lower running time) compared to Single-Objective Search and <em>De-MO</em>, respectively. For some crashes, the improvements are very large, being up to +93.3% for reproduction ratio and -92% for the required running time.
DOI: 10.5281/zenodo.7051255
title: Raw data for 'Increasing the Modulation Depth of Gd(III)-based Pulsed Dipolar EPR Spectroscopy (PDS) with Porphyrin-GdIII Laser Induced Magnetic Dipole Spectroscopy'

description: Raw data for 'Increasing the Modulation Depth of Gd(III)-based Pulsed Dipolar EPR Spectroscopy (PDS) with Porphyrin-GdIII Laser Induced Magnetic Dipole Spectroscopy'
DOI: 10.21227/553g-yn97
title: EEG Brainwave Dataset

description: The dataset is collected for the purpose of investigating how brainwave signals can be used to industrial insider threat detection. The dataset was connected using Emotiv Insight 5 channels device. The dataset contains data from 17 subjects who accepted to participate in this data collection.
DOI: 10.21227/9w13-2t13
title: Malicious Traffic in IoT Healthcare

description: The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment.
DOI: 10.21227/q0mg-6961
title: MIMIC dataset for Anomaly detection

description: The dataset is part of the MIMIC databaseand specifically utilise the data corresponding to twopatients with ids 221 and 230.
DOI: 10.21227/sf59-sz80
title: IoT-BDA Botnet Analysis Dataset

description: The proliferation of insecure Internet-connected devices gave rise to the IoT botnets which can grow very large rapidly and may perform high-impact cyber-attacks. To facilitate the improvement and the development of host and network-based IoT botnet detection solutions, and Linux malware analysis tools and methods, we provide the IoT-BDA Botnet Analysis Dataset. The dataset comprises the results of the analysis conducted by IoT-BDA Framework on 4077 unique IoT botnet samples captured by honeypots. The framework executed the samples in a sandbox and performed static, behavioural and network analysis to identify indicators of compromise and attack, anti-static-analysis, anti-dynamic-analysis, anti-forensics and persistence techniques used by IoT botnets. Each of the analysed samples was scanned using Virustotal and was attributed the most probable malware family it belongs to using the AVClass malware classifier. The dataset may also enable clustering of IoT botnet samples based on static, behavioural and network features derived by the framework. In addition to the analysis results, the dataset includes the botnet samples (ELF files), the captured behaviour (systemcalls) and the recorded network traffic (.pcap).
DOI: 10.21227/skhs-0b39
title: BlueTack

description: Bluetooth communication is widely adopted in IoMT devices due to its various benefits. Nevertheless, because of its simplicity as a personal wireless communication protocol, Bluetooth lacks the security mechanisms which may result in devastating outcomes for patients treated using wireless medical devices.
DOI: 10.25398/rd.northumbria.14053985
title: Database of results. Materials and techniques for coating of the nineteenth-century plaster casts. A scientific and archival investigation of the Victoria & Albert Museum cast collection.

description: Results of PhD research. Archival investigation, characterization of the V&A casts, reconstruction science and cleaning tests.
DOI: 10.5281/zenodo.3302023
title: Binary black-hole simulation SXS:BBH:1107

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.6841250
title: Data used for Investigating the role of Amazonian mesoscale wind patterns and strength on the spatial distribution of Martian bedrock exposures

description: This upload contains four distinct zipped files with several different datasets contained within used for the analysis in publication "Investigating the role of Amazonian mesoscale wind patterns and strength on the spatial distribution of Martian bedrock exposures" by Gary-Bicas et al., 2022 Description for each dataset is below. - External data (Contains data used for thermophysical and morphological analysis). binary files NBmap2007.bin (Putzig and Mellon, 2007) and nmap2003.bin( Putzig et al., 2005) . These contain global Mars thermal inertia maps using the Thermal Emission Spectrometer (TES) onboard Mars Global Surveyor (MGS) Comma separated files with terminations "...USGS.csv" these are files extracting data for the studies' regions from the Mars global USGS geologic map #3292 (Tanaka et al., 2014) Shape file for bedrock designations (bedrock.[shp,shx,prj,dbf]) created by Cowart et al., 2019 where they mapped locations with bedrock exposures on Mars. We also include comma separated value files of the same maps for locations studied in this analysis (files with termination "...bedrock.csv") Shape file included has the locations of craters identified in all study regions for analysis (craters.point.[shp,shx,prj,dbf] and craters.polygon.[shp,shx,prj,dbf]) paired with the comma separated value intracrat.csv Shape file with study locations for analysis (windo_modeling_locations_revised3.[shp,shx,prj,dbf]) - MRAMS data files.zip Contains 11 simulated climate states for each of the ten study location in analysis as well as Jezero crater using the Mars Regional Atmospheric Modeling System (MRAMS, Rafkin and Michaels, 2019). For each simulated case there are 4 seasonal time steps equating to 44 simulated cases for each study region in total (484 files) see associated python software publication indicating ingestion and processing of MRAMS datasets - MRAMS output files.zip After ingesting the datasets in MARS data files.zip into a python algorithm (see associated software publication) values for Wind Erosion Potential were extracted from the datasets and weighted sums were conducted to get annual values (see manuscript publication and associated python software publication,"MRAMS Data Output.ipynb") data was output into comma separated values for ease of use -MRAMS elevation and slope files.zip MRAMS data from output files.zip was further ingested into other algorithms to extract elevation and terrain slope values (see associated python software publication, "MRAMS Data Output.ipynb") that were output into comma separated values for ease of use
DOI: 10.5285/786b3ce6be54468496a3e11ce2f2669c
title: HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.0.3.0 (1862-2020)

description: HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The datasets cover the UK at 1 km x 1 km resolution. These 1 km x 1 km data have been used to provide a range of other resolutions and across countries, administrative regions and river basins to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution. The gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost. This data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation). This release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset. The primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project "Analysis of historic drought and water scarcity in the UK". The dataset is provided under Open Government Licence.
DOI: 10.21227/ss99-8d56
title: Drone Remote Controller RF Signal Dataset

description: This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms. The dataset is used to train machine learning and deep neural network models for the detection and classification of the drones/drone RCs.
DOI: 10.5281/zenodo.1423742
title: Compression Tests And Direct Shear Test Of Two Types Of Railway Ballast

description: This data set contains measurement data from uniaxial compression tests and direct shear tests<br>
 conducted on two types of railway ballast.<br>
For a detailed description of the experiments see:

B. Suhr, S. Marschnig and K. Six:<br>
"Comparison of two different types of railway ballast in compression and direct shear tests:<br>
experimental results and DEM model validation"<br>
Granular Matter (2018)<br>
Doi: 10.1007/s10035-018-0843-9

For the uniaxial compression tests, measured normal forces and vertical paths are provided.<br>
The direct shear tests are conducted directly afterwards, i.e. the information for<br>
one compression and one shear test are contained in only one file.<br>
For the shear tests shear paths, shear forces and vertical path are provided.

At first the uniaxial compression test is carried out. At the end of this test, the normal load is equal to zero.<br>
For the following direct shear test, the normal load is constant (according to the load specified in the file name)<br>
and not recorded in the file.<br>
The direct shear test starts, when the measured shear path is greater than zero.

Check the README.txt file for more information.
DOI: 10.5281/zenodo.2529721
title: Soil organic carbon stock (0–30 cm) in kg/m2 time-series 2001–2015 based on the land cover changes

description: Estimated SOC loss based on the European Space Agency (ESA) Climate Change Initiative (ESACCI-LC) land cover maps 2001–2015. This only shows estimated SOC loss (in kg/m2) as a result of change in land use / land cover (assuming standard change factors based on the literature and IPCC reports). Methodology produced for the purpose of the Land Degradation Neutrality (UNCCD) project. Processing steps are described in detail <strong>here</strong>. Antartica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: sol = theme: soil, organic.carbon.stock = variable: soil organic carbon stock in kg/m2, msa.kgm2 = determination method: derived from carbon content, bulk density and coarse fragments, m = mean value, 250m = spatial resolution / block support: 250 m, b0..30cm = vertical reference: standard layer 0-30 cm below surface, 2014 = time reference: year 2014, v0.2 = version number: 0.2,
description: {"references": ["Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0."]}