cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4548870
title: UWB Localization Dataset

description: The dataset contains information concerning metrics provided by a pair of Decawave DW1000 tranceiver ICs. The dataset is recorded in indoor (hallway) and outdoor spaces. Each record of the dataset has the following fields: - <strong>sequence_number:</strong> A sequence number indicating the order the measurements were taken - <strong>environment</strong>: The type of environment used for the measurements (indoor/outdoor) - <strong>channel:</strong> The UWB channel used by the transceivers - <strong>pac_size</strong>: The preamble sequence is detected by cross-correlating in chunks which are a number of preamble symbols long. The size of chunk used is the PAC size. - <strong>prf:</strong> The pulse repetition frequency used in MHz - <strong>preamble_length</strong>: The number of symbols used in the preamble - <strong>datarate</strong>: The datarate in Mbps - <strong>first_path_power</strong>: The received power in dBm of the pulse corresponding to the first path. - <strong>rssi</strong>: The Received Signal Stength in dBm - <strong>distance</strong>: The distance between the receiver and the transmitter in meters
DOI: 10.5281/zenodo.4641555
title: ChinaHighPM2.5: Big Data Full-coverage Ground-level PM2.5 L3 Yearly 0.1 Deg Product

description: ChinaHighPM<sub>2.5</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the Big Data Level 3 (L3) yearly 0.1 degree (≈ 10 km) gridded <strong>full-coverage</strong> ground-level PM<sub>2.5</sub> products in Eastern China (ECHAP_PM2.5_Y10K) from 2013 to 2020, which are averaged from Level 2 daily products. This dataset has high accuracy with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.91 and a root-mean-square error (RMSE) of 12.67 µg m<sup>-3</sup> on a daily basis. <strong>Note that this dataset is closed accessed since a higher 1 km ChinaHighPM<sub>2.5</sub> dataset with higher quality is available at: http://doi.org/10.5281/zenodo.6406856</strong> <strong>More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html</strong>
description: Email: weijing_rs@163.com; weijing.rs@gmail.com.
description: {"references": ["Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136"]}
DOI: 10.5281/zenodo.4678179
title: Sichuan Basin dipsersion data

description: We obtained a 3-D isotropic and azimuthal anisotropic model in the Sichuan Basin and adjacent areas. These datasets contain the dispersion data we picked, and the models we obtained.
DOI: 10.5281/zenodo.4771240
title: Projections of Mekong River discharge, and sea level rise along the Mekong Delta coast

description: Projections of Sea Level Rise (SLR) under RCP 4.5 and RCP 8.5 (AR5) along the Mekong Coast, Published<sup>1</sup> by the Ministry of Natural Resources and Environment (MONRE), Hanoi, Vietnam. Projections of Mekong River discharge during the <strong>dry season</strong> under RCP 4.5 and RCP 8.5 at Kratie, Cambodia. The data contains the cumulative, minimum and maximum dry season (January-1<sup>st</sup> to April-30<sup>th</sup>) discharge from 5 different climate models. PCR-GLOBWB<sup>2</sup> was run at 5 arc-min spatial resolution and forced with the data based on output from five ISIMIP CMIP5 global climate models (HadGEM2-ES, GFDL-ESM2, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M). 1. Ministry of Natural Resources and Environment (MONRE), V. <em>Climate change and sea level rise scenarios for Vietnam, Ministry of Natural Resources and Environment</em>. (2016). 2. Sutanudjaja, E. H. <em>et al.</em> PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model. <em>Geosci. Model Dev.</em> <strong>11</strong>, 2429–2453 (2018).
description: {"references": ["Sutanudjaja et al. (2018)", "Ministry of Natural Resources and Environment (2016)"]}
DOI: 10.5281/zenodo.3497360
title: Rotten Tomatoes User Reviews

description: Rotten tomatoes user reviews from Iraq War movies.
DOI: 10.1594/pangaea.676946
title: Benthic faunal biomass values off northern Peru, 1984, Peru
DOI: 10.1594/pangaea.929370
title: Calibrated ages of Zoige Wagen (ZB08-s4) peat core from Tibetan Plateau, China

description: Calibrated ages of a high-resolution peat core from the Past Global Changes - Carbon in Peat on EArth through Time (PAGES_C-PEAT) Project.
DOI: 10.5281/zenodo.3950417
title: DSDP Leg 84, Hole 568 - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: DSDP Leg 84: Mid America Trench Slope
DOI: 10.5281/zenodo.4588099
title: Meteorological and ground observations in the Qinghai-Tibet Engineering Corridor

description: The observations of meteorological factors were carried out from two permanent meteorological stations (Golmud and Wudaoliang) and one field meteorological station (Xidatan) with daily meteorological records for the period 2014-2018. Ground temperature and moisture data from the near-surface to within 270 cm in the active layer were recorded for the permafrost slope.
DOI: 10.1594/pangaea.777428
title: Well-logging measurements from IODP Hole 319-C0009A

description: Geocode = [m, CMP] Top Depth DSF, MSF, WSF and CSF-B. MWD = measurement while drilling.
DOI: 10.5281/zenodo.4613124
title: UWB Motion Detection Data Set

description: <strong>Introduction</strong> This data set includes a collection of measurements using DecaWave DW1000 UWB radios in two indoor environments used for motion detection functionality. Measurements include channel impulse response (CIR) samples in form of power delay profile (PDP) with corresponding timestamps for three channels for each indoor environment. Data set includes pieces of Python code and Jupyter notebooks for data loading, analysis and to reproduce the results of a paper entitled "UWB Radio Based Motion Detection System for Assisted Living" submitted to MDPI Sensors. The data set will require around 10 GB of total free space after extraction. The code included in the data set is written and tested on Linux (Ubuntu 20.04) and requires 16 GB of RAM and additional SWAP partition to run properly. The code can be modified to consume less memory but it requires unnecessary additional work. If the .npy format is compatible with your numpy version, you won't need to regenerate npy data from .csv files. <strong>Data Set Structure</strong> The resulting folder after extracting the uwb_motion_detection.zip file is organized as follows: <strong>data</strong> subfolder: contains all original .csv and intermediate .npy data files. models pdp: this folder contains 4 .csv files with raw PDP measurements (timestamp + PDP). The data format will be discussed in the following section. pdp_diff: this folder contains .npy files with PDP samples and .npy files with timestamps. Those files are generated by running the generate_pdp_diff.py script. generate_pdp_diff.py validation subfolder: contains data for motion detection validation events: contains .npy files with motion events for validation. The .npy files are generated using generate_event_x.py files or notebooks inside the /Process/validation folder. pdp: this folder contains raw PDP measurements in .csv format. pdp_diff: this folder contains .npy files with PDP samples and .npy files with timestamps. Those files are generated by running the generate_pdp_diff.py script. generate_events_0.py generate_events_1.py generate_events_2.py generate_pdp_diff.py <strong>figures</strong> subfolder: contains all figures generated in Jupyter notebooks inside the "Process" folder. <strong>Process</strong> subfolder: contains Jupyter notebooks with data processing and motion detection code. MotionDetection: contains notebook comparing standard score motion detection with windowed standard score motion detection OnlineModels: presents the development process of online models definitions PDP_diff: presents the basic principle of PDP differences used in the motion detection Validation: presents a motion detection validation process <strong>Raw data structure</strong> All .csv files in data folder contain raw PDP measurements with timestamps for each PDP sample. The structure of file goes as follows: unix timestamp, cir0 [dBm], cir1 [dBm], cir2[dBm] ... cir149[dBm]
DOI: 10.1594/pangaea.378665
title: Radiosonde PS15/05190 during POLARSTERN cruise ARK-VI/1 on 1989-05-05 04:34h

description: Height of tropopause 7388 m, precipitable water content 1.0829 cm
DOI: 10.5281/zenodo.5758743
title: Data for "On the difference of aerosol hygroscopicity between high and low RH conditions in the North China Plain"

description: Data for "Measurement Report: On the difference of aerosol hygroscopicity between high and low RH conditions in the North China PlainMeasurement Report: On the difference of aerosol hygroscopicity between high and low RH conditions in the North China Plain"
DOI: 10.5281/zenodo.6555241
title: Code Smells Dataset (oracles)

description: This repository contains the datasets, obtained in 3 years, resulting from the Crowdsmelling methodology.<br> Each file contains the dataset (oracle) of the year or set of years, for the code smells Long Method, God Class, and Feature Envy. The file Exercise-Code smells detection (ESII 2020).pdf describes the exercise used in the validation of code smells, and the file code-classification-statistics.csv shows statistics about the percentages of teams that classified the methods and classes. More information about the datasets can be found in the article: Reis, José Pereira dos , Abreu, Fernando Brito e . & Carneiro, Glauco de Figueiredo. Crowdsmelling: A preliminary study on using collective knowledge in code smells detection. <em>Empir Software Eng</em> <strong>27, </strong>69 (2022). https://doi.org/10.1007/s10664-021-10110-5 DATASET STRUCTURE<br> - project name<br> - package name<br> - class name<br> - method name<br> - code metrics [1]<br> - code smell classification REFERENCES [1] Metrics description can be found in the study: "Fontana, F. A., Mantyla, M. V., Zanoni, M., and Marino, A. (2015), Comparing and experimenting machine learning techniques for code smell detection, Empirical Software Engineering"
DOI: 10.5281/zenodo.4498296
title: Before-, during-, and after-flood UAV-generated digital elevation models, orthomosaics, and GIS datasets of the distal part of Zackenberg river, northeast Greenland (August 2017)

description: This dataset contains a time series of very high-resolution dataset derived from UAV-generated images. Data covers the distal part of Zackenberg River, northeast Greenland. Images were captured using an unmanned aerial vehicle (UAV) immediately before (5<sup>th</sup> August 2017), during (6<sup>th</sup> August 2017), and after (8<sup>th</sup> August 2017) the glacier lake outburst flood. UAV-generated images were processed using the structure-from-motion approach in Agisoft Metashape software. Resultant products include digital elevation models, orthomosaics, and hillshade models. The second group of files (GIS_data.zip) contains vector files resultant from geomorphological mapping. Potential applications of the presented dataset include: assessment and quantification of landscape changes as an immediate result of glacier lake outburst flood; long-term monitoring of high-Arctic river valley development (in conjunction with other datasets); establishing a baseline for quantification of geomorphological impacts of future glacier lake outburst floods; assessment of geohazards related to bank erosion and debris flow development (hazards for research station infrastructure – station buildings and bridge); monitoring of permafrost degradation; and modelling flood impacts on river ecosystem, transport capacity, and channel stability. This dataset contains photogrammetric products derived from the UAV-generated images (orthomosaics, digital elevation models, hillshade models) and GIS products. Unprocessed images, which were the basis for structure-from-motion processing, can be accessed at: https://doi.org/10.5281/zenodo.4495282 (Tomczyk and Ewertowski, 2021) Tomczyk A.M., Ewertowski M.W., 2021: Before-, during-, and after-flood UAV-generated images of the distal part of Zackenberg river, northeast Greenland (August 2017), https://doi.org/10.5281/zenodo.4495282
description: The research leading to these results has received funding from the European Union's Horizon 2020 project INTERACT, under grant agreement No. 730938, project number: 119 [ArcticFan].
DOI: 10.5281/zenodo.2586940
title: N-gram dataset of Xu Xiu Si Ku Quan Shu (續修四庫全書)

description: This dataset contains the N-grams (1-3) collected from Xu Xiu Si Ku Quan Shu (續修四庫全書).

The dataset comprises of the following resources:


	<strong>xuxiu<strong>_</strong>1.7z</strong> Unigram dataset in tab seperated format (one file per book, each row contains the N-gram and its count)
	<strong>xuxiu</strong><strong>_2.7z</strong> Bigram dataset in tab seperated format (one file per book, each row contains the N-gram and its count)
	<strong>xuxiu</strong><strong>_3.7z</strong> Trigram dataset in tab seperated format (one file per book, each row contains the N-gram and its count)
	<strong>xuxiu_metadata.xlsx</strong> Metadata of each book


 

Dieses Datenset enthält die im Xu Xiu Si Ku Quan Shu (續修四庫全書) enthaltenen N-Gramme (1-3).  

Das Datenset besteht aus den folgenden Dateien:


	<strong>xuxiu_1.7z</strong> Monogramm-Datenset im .txt Dateiformat mit Tabstopp als Trennzeichen (jede Datei enthält ein Buch, jede Zeile ein N-Gramm mit der Anzahl der Vorkommnisse im Text)
	<strong>xuxiu_2.7z</strong> Bigramm-Datenset im .txt Dateiformat mit Tabstopp als Trennzeichen (jede Datei enthält ein Buch, jede Zeile ein N-Gramm mit der Anzahl der Vorkommnisse im Text)
	<strong>xuxiu_3.7z</strong> Trigramm-Datenset im .txt Dateiformat mit Tabstopp als Trennzeichen (jede Datei enthält ein Buch, jede Zeile ein N-Gramm mit der Anzahl der Vorkommnisse im Text)
	<strong>xuxiu_metata.xlsx</strong> Metadaten der enthaltenen Bücher


 

《續修四庫全書》n元語法統計資料 (N-gram Dataset)

以下是檔案簡說:


	<strong>xuxiu</strong><strong>_1.7z</strong> 《續修四庫全書》一元分詞(Unigram)的統計資料 (每本書一個檔案,以tab作欄區分,每一行紀錄該 N-gram 在書中出現的次數)
	<strong>xuxiu</strong><strong>_2.7z</strong> 《續修四庫全書》二元分詞(Bigram)的統計資料 (每本書一個檔案,以tab作欄區分,每一行紀錄該 N-gram 在書中出現的次數)
	<strong>xuxiu</strong><strong>_3.7z</strong> 《續修四庫全書》三元分詞(Trigram)的統計資料 (每本書一個檔案,以tab作欄區分,每一行紀錄該 N-gram 在書中出現的次數)
	<strong>xuxiu</strong><strong>_metadata.xlsx</strong> 紀錄每本書的基本Metadata
DOI: 10.5281/zenodo.4879494
title: Meteorological and ground observations in the Qinghai-Tibet Engineering Corridor

description: The observations of meteorological factors were carried out from two permanent meteorological stations (Golmud and Wudaoliang) and one field meteorological station (Xidatan) with daily meteorological records for the period 2014-2018. Ground temperature and moisture data from the near-surface to within 270 cm in the active layer were recorded for the permafrost slope.
DOI: 10.5281/zenodo.1213026
title: A Performance Evaluation Of The World Wide Lightning Location Network (Wwlln) Over The Tibetan Plateau

description: The data is associated with the paper titled "A Performance Evaluation of the World Wide Lightning Location Network (WWLLN) over the Tibetan Plateau" which is published in Journal of Atmospheric and Oceanic Technology with the doi: 10.1175/JTECH-D-17-0144.1. The abstract is as follows:

A systematic evaluation of the performance of the World Wide Lightning Location Network (WWLLN) over the Tibetan Plateau is conducted using data from the Cloud-to-Ground Lightning Location System (CGLLS) developed by the State Grid Corporation of China for 2013–15 and lightning data from the satellitebased Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) for 2014–15. The average spatial location separation magnitudes in the midsouthern Tibetan Plateau (MSTP) region between matched WWLLN and CGLLS strokes and over the whole Tibetan Plateau between matched WWLLN and LIS flashes were 9.97 and 10.93 km, respectively. The detection efficiency (DE) of the WWLLN rose markedly with increasing stroke peak current, and the mean stroke peak currents of positive and negative cloud-toground (CG) lightning detected by theWWLLNin the MSTP region were 62.43 and256.74 kA, respectively. The duration, area, and radiance of the LIS flashes that were also detected by the WWLLN were 1.27, 2.65, and 4.38 times those not detected by the WWLLN. The DE of the WWLLN in the MSTP region was 9.37% for CG lightning and 2.58% for total lightning. Over the Tibetan Plateau, the DE of the WWLLN for total lightning was 2.03%. In the MSTP region, theCGflash data made up 71.98% of allWWLLNflash data. Based on the abovementioned results, the ratio of intracloud (IC) lightning to CG lightning in the MSTP region was estimated to be 4.05.
DOI: 10.5281/zenodo.3245183
title: Seismic data (ASCII)

description: Each file corresponds to a single day of data.

Signal amplitude (0± 2048) of the 6 sensors (on each line) at a sampling rate of 250 Hz.
DOI: 10.5281/zenodo.3339638
title: Dataset used in publication "The Climatic Effects of Hygroscopic Growth of Sulfate Aerosols in the Stratosphere"

description: Essential model data used to produce the figures and tables in publication "The Climatic Effects of Hygroscopic Growth of Sulfate Aerosols in the Stratosphere<strong>"</strong>
DOI: 10.1594/pangaea.777313
title: Well-logging measurements from IODP Hole 314-C0006B

description: Geocode = [m, CMP] Top Depth DSF, MSF, WSF and CSF-B. MWD = measurement while drilling.
DOI: 10.1594/pangaea.938756
title: Interpolated Lake Bathymetry of Lake Lucerne

description: This dataset provides the interpolated bathymetry of Lake Lucerne used for the numerical wave propagation simulation performed in the study “Shallow-Water Tsunami Deposits: Evidence from Sediment Cores and Numerical Wave Propagation of the 1601 CE Lake Lucerne” by Nigg et al. (in review). The multibeam echo-sounder bathymetry dataset of Lake Lucerne acquired by Hibe et al. (2011) was resampled from a grid size of 1x1 m to 5x5 m to reduce computational time using ArcMap (version 10.8.1). In addition, large artificial shoreline modifications were cropped and interpolated based on historical maps. Shallow-water areas (water depth 0-4m), which are not entirely covered by the bathymetrical data were linearly interpolated to the shoreline.The original bathymetry was acquired using a Geo-Acoustics GeoSwath Plus 125 kHz interferometer by Hilbe et al. (2011). Positioning was acquired with a Leica SR 530 GPS receiver with real time kinematic positioning (RTK; swipos GIS/GEO from swisstopo). Acquisition control and data processing were conducted using the GeoAcoustics GS+ software package. Swiss basic hydrological monitoring network (BAFU, 2008) were used to normalize water depths to the mean lake level (433.6 m a.s.l.). See Hilbe et al. (2011) for further information. The original bathymetry dataset is available from swisstopo and should be referenced as Hilbe, M., Anselmetti, F. S., Eilertsen, R. S., Hansen, L., & Wildi, W. (2011). Subaqueous morphology of Lake Lucerne (Central Switzerland): implications for mass movements and glacial history. Swiss Journal of Geosciences, 104(3), 425-443.
DOI: 10.5281/zenodo.556234
title: Http(S) Traffic Data

description: This dataset contains HTTP/HTTPS raw traffic data collected by crawling top 2,165 websites using VPN vantage points from 52 countries. The objective of crawling each website from different countries is to discover various geographic-specific hostnames used by the websites to serve content across geography. 

The capitalized alphabet in the filename, e.g., A in dataA.tar.gz, represent the country names starting with that alphabet from were the data was collected.

This dataset was collected in January, 2013.
DOI: 10.5281/zenodo.3631548
title: Time series of annual TAS 40-year trend from historical to future in CMIP5 model simulations

description: Time series of 40-year linear trend for the annual near surface air temperature (tas) for the period 1979-2100 as simulated by the CMIP5 models on a 2 x 2 deg grid. The data for the period 1979-2005 are taken from the CMIP5 historical simulations, while data for 2006-2100 are from the CMIP5 future scenario rcp2.6, rcp4.5 and rcp8.5, respectively. The 40 year trend of 1979-2018 from the ERA-Interim reanalysis is also provided in a separate file. Each file contains the time series of the 40-year linear trend for tas at each grid point from historical to one of the three scenarios simulated by one model. The linear trend is calculated using cdo, and the year associated with each data point in a file corresponding to the last year in the 40-year time period, e.g, the associated year 2018 corresponds to the linear trend calculated for the period 1979-2018. Unit: C/year.
DOI: 10.5281/zenodo.4616462
title: The ECS-FVCOM results and also the input variables of FMGDM, the initial particles, satellite pictures, and trajectories data

description: The data for the manuscript: A Lagrangian-based Floating Macro<br> -algal Growth and Drift Model (FMGDM v1.0): application to the Yellow Sea green tides. <br> -----------------------------------------------------------------------------<br> Updata: 2021/03/18 -----------------------------------------------------------------------------<br> Part 1: The ECS-FVCOM results and also the input variables of FMGDM<br> Part 2: The initial particles position imformation (lat, lon, depth)<br> Part 3: The satellite pictures of green tides in YS, 2014 and 2015 Part 4: Drifters trajectories dataset (lat, lon) ============================================= Version 5 updata: 2021/12/20 Note: Modified and added some missing variables ('omega') in Part1, the results of ECS-FVCOM