cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.2669484
title: Atom probe tomography nomad-FAIR demonstrator dataset R76-20231-v01.epos.apth5

description: This is the dataset of an atom probe tomography experiment which is provided open source for testing the possibility of implementing an open source encyclopedia for experimental materials science datasets, including techniques to begin with such as Scanning Transmission Electron Microscopy (STEM), Multidimensional Photo Emission Spectroscopy (MPES), and Atom Probe Tomography (APT) / Field Ion Microscopy (FIM).

<strong>This repository serves three aims:</strong>

1. The dataset is of scientific interest. Specifically, it captures the result of a cutting-edge APT experiment detailed exemplarily in DOI 10.1017/S1431927616012654 Fig. 1d by Zirong Peng and coworkers.

2. The dataset contributes to tests of an extension to "The NOMAD Laboratory" (https://nomad-coe.eu/): nomad-FAIR. Specifically, to test various aspects of an automatized metadata parsing and processing pipeline to enable the extraction of domain-specific JSON metadata files into a NOMAD-conformant JSON file, ultimately aiming for searchable and repurposable dataset documentation. This serves two purposes: on the one hand to contextualize each dataset within NOMAD. On the other hand to serve as a starting point to parse potential interesting content from the heavy data HDF5 file to reduce unnecessary file access.

The implementation of nomad-FAIR is coordinated by Markus Scheidgen.<br>
The APT domain-specific parser is developed by Markus Kühbach.

3. The dataset constitutes further a test of an open format specification for storing atom probe tomography data using the Hierarchical Data Format (HDF5). This is a recent initiative of the International Field Emission Society's (IFES) atom probe tomography technical committee. In this repository it is detailed an exemplar proposal of how to store acquisition-side relevant results and context of an APT experiment into a HDF5 file and complementary metadata files such as JSON. Implementation of this HDF5-based storage solution for APT data is lead by Markus Kühbach.

<br>
<strong>The organization of this repository with respect to above aims is as follows:</strong>

-The original EPOS file of the measured is contained in the compressed *.epos.tar.gz archive.

-The *.apth5 file is a transcoded version of the EPOS file. Therein, x,y,z data columns are stripped.

-The correspondingly named *.json file is the file which nomad-FAIR parses metadata from.

-Other files constitute logs of the transcoding process.

<strong>Funding:</strong><br>
The work was partially supported by BiGmax, the Max Planck Society's Research Network on Big-Data-Driven Materials-Science.
DOI: 10.5281/zenodo.4769491
title: A small climate-amplifying effect of climate-carbon cycle feedback

description: This is the code for the work in the paper "A small climate-amplifying effect of climate-carbon cycle feedback" by Xuanze Zhang et al., 2021. This code is used to quantify the carbon-concentration feedback parameter (beta) and carbon-climate feedback parameter (gamma) of the global climate-carbon cycle feedback on different timescales (1/frequencies) for a time period with inputs of global CO2 emssions, atmospheric CO2 concentration and global mean surface air temperature time series.
DOI: 10.5281/zenodo.5120960
title: Seer Breast Cancer Data

description: <strong>Abstract</strong>: This dataset of breast cancer patients was obtained from the 2017 November update of the SEER Program of the NCI, which provides information on population-based cancer statistics. The dataset involved female patients with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3) diagnosed in 2006-2010. Patients with unknown tumor size, examined regional LNs, regional positive LNs, and patients whose survival months were less than 1 month were excluded; thus, 4024 patients were ultimately included. <strong>Inspiration:</strong> This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON" https://cancer.ubrite.org/hackathon-2021/ <strong>Acknowledgements</strong> JING TENG, January 18, 2019, "SEER Breast Cancer Data", IEEE Dataport, doi: https://dx.doi.org/10.21227/a9qy-ph35. https://ieee-dataport.org/open-access/seer-breast-cancer-data <strong>U-BRITE last update date:</strong> 07/21/2021
description: U-BRITE location: /data/project/ubrite/cancer-hackathon/org/ieee-dataport/seer-breast-cancer-data
DOI: 10.5281/zenodo.58229
title: Hoarding And Horizontal Transfer Led To An Expanded Gene And Intron Repertoire In The Plastid Genome Of The Diatom, Toxarium Undulatum (Bacillariophyta)

description: Multiple sequence alignments used to produce Figure 2
DOI: 10.5281/zenodo.2277818
title: RADICLE_S355_test_data

description: The RADICLE_S355_test_data dataset consists on the raw and treated data related to testing the laser welding process on S355 steel (a high-strength low-alloy structural grade), in a butt-weld configuration.

S355 is a material commonly applied throughout the ‘heavy industry’ sectors, including transport (road, rail and marine), yellow goods (earth-moving and construction machinery), civil engineering and energy sectors. Hence, the H2020 RADICLE project consortium is providing open access to the data collected during the test trials, to allow for further analysis and re-use of this information.
DOI: 10.5281/zenodo.3548750
title: 10.1103PhysRevLett.123.167201

description: Data set for Paper published in Physical Review Letters: Phys. Rev. Lett. <strong>123</strong>, 167201 – Published 14 October 2019
DOI: 10.5281/zenodo.3743863
title: List of Facade Fires 1990 - 2020

description: This is a list of high-rise building fires where the fire spread in some way via the facade of the building. The data was found through searching Google for news reports and the fire safety literature. It is therefore likely to be biased towards high-profile events, and may have missed some smaller fires.<br> The data from this table has been used in the following: *M. Bonner, G. Rein, Flammability and multi-objective performance of building facades: towards optimum design, International Journal of High-Rise Buildings, 2018. https://doi.org/10.21022/IJHRB.2018.7.4.363 *M. Bonner, W. Wegrzynski, B. Papis, G. Rein, Kresnik: A top-down, statistical approach to understand the fire performance of building facades using standard test data, Building and Environment, 2019. https://doi.org/10.1016/j.buildenv.2019.106540
description: This list might be updated as new data is collected.
DOI: 10.5281/zenodo.4495282
title: Before-, during-, and after-flood UAV-generated images of the distal part of Zackenberg river, northeast Greenland (August 2017)

description: This dataset contains a time series of very high-resolution images of 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. Data cover a 2.1-km-long distal section of Zackenberg river, from the delta to the bridge. Images were captured with high overlap; therefore, they can be processed in photogrammetric software. They are georeferenced using UAV onboard GNSS system and can be coregistered using a model-to-model approach or external ground control points. Images are organized into three zipped files, according to the capture date. 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 unprocessed images. Photogrammetric products derived from the images (orthomosaics, digital elevation models, hillshade models) and GIS products, which are the results of the geomorphological mapping, can be accessed at: https://doi.org /10.5281/zenodo.4498296 (Tomczyk and Ewertowski, 2021) Tomczyk A.M., Ewertowski M.W., 2021: 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), https://doi.org /10.5281/zenodo.4498296
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.4549766
title: Data associated with the publication titled "Lower Tropospheric Processes; A Control on The Global Mean Precipitation Rate"

description: Dataset that is used in the journal article <em>Lower Tropospheric Processes; A Control on The Global Mean Precipitation Rate. Here, the user will find the output for a single model experiment using the Community Atmosphere Model Version 5.0. The monthly output from the experiment consists of 5 model runs where each simulation has a different threshold that controls the formation of low-clouds. The tuned parameter is Rhminl. This parameter was tuned in order to indirectly modulate lower-tropospheric mixing. </em>
DOI: 10.5281/zenodo.4661573
title: A High-Accuracy GNSS Dataset of Ground Truth Points Collected within Îles-de-Boucherville National Park, Quebec, Canada

description: This dataset contains high accuracy (&lt; 10 cm error) GNSS ground truth measurements of the invasive reed <em>Phragmites australis </em>subsp. <em>australis </em>within Îles-de-Boucherville National Park (Quebec, Canada). The park, which has extensive stands of <em>P. australis </em>throughout the area, is one of the five study sites for the Canadian Biodiversity Airborne Observatory (CABO). The data were originally collected to serve as training and validation data for airborne hyperspectral imagery acquired in 2019 to assist with the detection and mapping of <em>P. australis </em>within the park, as no previous ground truth data had been collected at this site with such accurate measurements. In total, 320 ground truth points were collected, representing 158 locations where <em>P. australis</em> was present and 162 locations where it was absent. Auxiliary data includes detailed field notes and field photographs that provide context for each point, as well as a shapefile (.shp) containing all 320 points.
DOI: 10.5281/zenodo.4679725
title: A Data set for Information Spreading over the News

description: <strong>Abstract:</strong> Analyzing the spread of information related to a specific event in the news has many potential applications. Consequently, various systems have been developed to facilitate the analysis of information spreadings such as detection of disease propagation and identification of the spreading of fake news through social media. There are several open challenges in the process of discerning information propagation, among them the lack of resources for training and evaluation. This paper describes the process of compiling a corpus from the EventRegistry global media monitoring system. We focus on information spreading in three domains: sports (i.e. the FIFA WorldCup), natural disasters (i.e. earthquakes), and climate change (i.e.global warming). This corpus is a valuable addition to the currently available datasets to examine the spreading of information about various kinds of events. <strong>Introduction:</strong> Domain-specific gaps in information spreading are ubiquitous and may exist due to economic conditions, political factors, or linguistic, geographical, time-zone, cultural, and other barriers. These factors potentially contribute to obstructing the flow of local as well as international news. We believe that there is a lack of research studies that examine, identify, and uncover the reasons for barriers in information spreading. Additionally, there is limited availability of datasets containing news text and metadata including time, place, source, and other relevant information. When a piece of information starts spreading, it implicitly raises questions such as as How far does the information in the form of news reach out to the public? Does the content of news remain the same or changes to a certain extent? Do the cultural values impact the information especially when the same news will get translated in other languages? <strong>Statistics about datasets:</strong> -------------------------------------------------------------------------------------------------------------------------------------- # Domain Event Type Articles Per Language Total Articles 1 Sports FIFA World Cup 983-en, 762-sp, 711-de, 10-sl, 216-pt 2679 2 Natural Disaster Earthquake 941-en, 999-sp, 937-de, 19-sl, 251-pt 3194 3 Climate Changes Global Warming 996-en, 298-sp, 545-de, 8-sl, 97-pt 1945 --------------------------------------------------------------------------------------------------------------------------------------
DOI: 10.5281/zenodo.1172669
title: Gnss Signal Samples In The L2 Band

description: GNSS signals captured in the L2 band.

Format: Baseband

Data Type: Interleaved short ( I & Q samples, 16-bit signed integer)

Sampling frequency: 3.125 MSps

Signal grabber: USRP X300

Date: February 12, 2018.
DOI: 10.5281/zenodo.2625737
title: Dataset accompanying Riesch et al. 2019. Grazing by wild red deer: management options for the conservation of semi-natural open habitats. Journal of Applied Ecology

description: This repository contains vegetation biomass and forage quality data used by Riesch et al. in an article accepted in Journal of Applied Ecology.

Metadata are provided in the first excel worksheet ('explanation_overview'). For further details please see the original article.
description: The project was supported by funds of German government's Special Purpose Fund held at Landwirtschaftliche Rentenbank [28 RZ 7007].
DOI: 10.5281/zenodo.3610124
title: Online Interactive Perception of Articulated Objects with Multi-Level Recursive Estimation Based on Task-Specific Priors -- Rosbag data

description: This is the rosbag data accompanying the IROS 2014 paper "Online Interactive Perception of Articulated Objects with Multi-Level Recursive Estimation Based on Task-Specific Priors"
DOI: 10.5281/zenodo.3754703
title: IODP Expedition 362 Photomicrographs

description: Microscopic images of discrete samples were acquired using stereo and upright light microscopes and captured on digital cameras. Image files were uploaded along with a brief description and a record of the microscopic and lighting conditions when the image was taken.
DOI: 10.5281/zenodo.4282115
title: Hail disaster data for 2014–2018 in the Beijing-Tianjin-Hebei region

description: This data set is a supplement to the journal article " Comparison of Spatiotemporal Distribution and Occurrence Conditions of Large and Small Hail Events in Beijing-Tianjin-Hebei Region". The data set consists of Hail disaster information included the date, duration and location, and the maximum size of hailstone.(Data_BTH_Hail_for_Submit.xlsx HE information) The atmospheric environment data from ERA-interim data associated with the hail events in this study. (Data_BTH_Hail_for_Submit.xlsx. Atmospheric parameters) VIL values correspond to each hailstorm. (HailStorm_VIL.zip)
DOI: 10.5281/zenodo.834173
title: Systematic Psi Processing Over Santorini Volcano: The 2012-2016 Post Unrest Period

description: In the framework of the ESA funded project “Disaster Risk Reduction using innovative data exploitation methods and space assets”, the National Observatory of Athens together with Terradue customized a GEP implementation of the StaMPS suite of s/w modules, to automatically process stacks of SAR imagery using Persistent Scatterer Interferometry. The engine is now able to seamlessly and timely process data from Envisat, ERS-1,2, TerraSAR-X, COSMO-SkyMed and Radarsat-2 platforms, for user-defined Regions Of Interest. Hence, the concept of project’s “Trial Case VO-1” is to modify, customize, execute and test a fully automatic processing chain over the GEP, starting from raw/SLC SAR data, to ground velocities generation.

The new capabilities have been tested for an historic volcano in Santorini island, Greece, aiming to create a unique database of diachronic InSAR measurements spanning from 1992 to 2015 (~23 years of data). The processing of the five different SAR stacks was done entirely within the GEP environment, in the context of the Trial Case VO-1. The thematic map included herein contains the estimated Line-Of-Sight ground velocities (mm/yr) for the <strong>period 2012-2016, based on 20 descending Radarsat-2 images</strong>. Kindly note that the product is raw and has not gone through any manual post-processing. Therefore, adjustments may need to be made to address reference point issues, isolated phase unwrapping errors, etc.
DOI: 10.5281/zenodo.3534393
title: Testing GNSS receiver accuracy in Samsung Galaxy series mobile phones at a sports stadium

description: Required dataset for the open acces manuscript.
DOI: 10.5281/zenodo.848325
title: Gnss Data From Irkutsk Stations During The 2015 June 22

description: Raw data from 2 GNSS stations in Irkutsk, Russia during the 2015 June 22. Irk_15JUN22_000000.rar contains S4, phase scintillations records; ISTP_2015_06_22_00.00.00.jps contains L1, L2, P1, P2 records. We upload GPStation-6 and Javad Delta-G3T data. It is raw data in .gps (https://www.novatel.com/assets/Documents/Manuals/om-20000132.pdf) and .GREIS (http://www.javad.com/jgnss/support/manuals.html) formats.
DOI: 10.5281/zenodo.163341
title: Tess Dataset - September 2016

description: This datasets corresponds to the measures taken by the STARS4ALL photometer network during the month of September(2016).
DOI: 10.5281/zenodo.4476489
title: Fixed-Wing Micro UAV Open Data With Digicam And Raw INS/GNSS - IGN Flight 8

description: The data set originate from a series of flights conducted with fixed-wing micro UAV carrying high-quality small camera and navigation sensors. This data was previously used in several peer-reviewed publications and will also be used in ISPRS workshop on dynamic networks given during the 2021 ISPRS Congress. This is part of a larger series of data that will be released gradually after incorporating user's feedback (e.g., on formats, description,etc.). The data set contains the sensor measurements from GPS, IMU and Camera.
DOI: 10.5281/zenodo.5721459
title: Modern China Geospatial Database - Republican China Dataset

description: <strong>MCGD_Rep</strong> is a sample of spatial data for China in the first half of twentieth century (1900-1949). The data was extracted from the MCGD Main Dataset. It is based mostly on the list of <em>xian</em> (county) seats in 1931 [Source: Zang, Lihe 臧励龢, ed. Zhongguo gujin diming da cidian 中国古今地名大辞典. Shanghai 上海: Commercial Press, 1931], with the addition of some external data [Source: Crow Newspaper Directories]. By and large, it presents a list of the major locations in China between 1900 and 1949. It contains 1,977 entries with the following variables: name in Chinese, name in pinyin; name of the province in Chinese and in pinyin; latitude and longitude, and Name ID and Location ID.
DOI: 10.5281/zenodo.4137957
title: East Coast Low Database Southeast Australia

description: This east coast low (ECL) database 1970-2019 adjacent to the south east coast of Australia updates the database from 1970-2006 (Speer et al. 2009). This new database is restricted to April-September and records the initial date that a ECL forms in the area, its latitude/longitude position, central pressure on initial development and lowest pressure (strongest intensity), as shown on the Australian Bureau of Meteorology archived MSLP charts. In addition, if the ECL experiences a 24 h pressure drop greater than or equal to 10 hPa at 00 UTC, the date immediately at the end of the pressure drop is recorded. The data were grouped in 7 synoptic categories, as in Speer et al. (2009), followed by a division of those 7 that formed either in an easterly or westerly wind regime. Speer, MS., Wiles, P., Pepler, A. 2009. <em>Low pressure systems off the New South Wales coast and associated hazardous weather: establishment of a database.</em> <strong>Aust. Met. Mag., 58</strong>, 29-39. Speer, M.S., Leslie, L.M., Hartigan, J., MacNamara, S. <em>Trends in east coast low pressure systems affecting the southeast of Australia. </em><strong>J. South. Hemisphere Earth Sys. Sci. </strong><em>(in review)</em>
DOI: 10.6084/m9.figshare.22187854.v8
title: Ling et al., 2023, JGR-ES Figure 7 dataset

description: Al2O3 and Fe2O3 contents of bauxite samples
DOI: 10.5281/zenodo.7643683
title: Dataset for Digital Twin paper

description: Description for each folder: 1) The "raw_signal_data" folder contains original sensor data. The origigital sensor data is named as "ACF-x-x.xlsx". In each excel document, three directions of vibration, current and force sensor data is included. Naming rules is as follows: "ACF-1-2.xlsx" represents the orginal sensor data from first layler's second slot in. "ACF-2-3.xlsx" represents the orginal sensor data from second layler's third slot. 2) The original real measured surface roughness is in the "Ra.xlsx". The corresponding cutting parameters for every raw signal data of each slot are shown in "Ra.xlsx" document as well. <br> 3) The prediction results from different models are save in "prediction_results". The detalies are listed below. "BPNN_all_signal.xlsx" is the prediction result from BPNN used the combination of all sensors data as the model input <br> "BPNN_current_signal.xlsx" is the prediction result from BPNN used only the current sensor data as the model input <br> "BPNN_force_signal.xlsx" is the prediction results from BPNN used only the force sensor data as the model input <br> "BPNN_vibration_signal.xlsx" is the prediction result from BPNN used only the vibration sensor data as the model input<br> "ELM.xlsx" is the prediction result from ELM used the combination of all sensors data as the model input <br> "GPR.xlsx" is the prediction result from GPR used the combination of all sensors data as the model input<br> "LASSO.xlsx" is the prediction result from LASSO used the combination of all sensors data as the model input<br> "MLR.xlsx" is the prediction result from MLR used the combination of all sensors data as the model input<br> "SVR.xlsx" is the prediction result from SVR used the combination of all sensors data as the model input<br> "cnn_all_signal.xlsx" is the prediction result from CNN used the combination of all sensors data as the model input <br> "cnn_current.xlsx" is the prediction result from CNN used only the current sensor data as the model input <br> "cnn_force.xlsx" is the prediction results from CNN used only the force sensor data as the model input <br> "cnn_vibration.xlsx" is the prediction result from CNN used only the vibration sensor data as the model input<br> "real_ytest.xlsx" is the real-measured surface roughness data for testing models.