cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25919/7zmy-ec48
title: CCS - MV Bluefin 2018 V02 Bioacoustics

description: This bio-acoustic data was collected during a November 2018 voyage on the Australian Maritime College training vessel MV Bluefin as part of the Carbon Capture and Storage project. The survey was conducted in the nearshore of the Gippsland region. See voyage report Four frequency bio-acoustic water column and seafloor backscatter. Simrad 38 kHz and 120 kHz echosounders are part of the Bluefin’s installed instrument suite. The 70 and 200 kHz echosounders are a temporary installation for this voyage. The transducers were mounted in a custom flange attached to the bottom of the vessel. 30 m transducer extension cables connect to the topside Simrad WBT Tube transceiver. Data was acquired using Simrad EK80 Version 1.12 software logging at EK60 resolution. Acoustic data were recorded in local time. Echosounder details: Echosounder | Frequency | Mode | Transceiver Serial no | Transducer Model & serial No | Comments Simrad EK60 | 38 kHz | CW | - | ES38B, SN30333 | Using CSIRO EK60 GPT on loan as MV Bluefin GPT was faulty. Simrad EK60 | 120 kHz | CW | - | ES120-7C, 120 | - Simrad WBT Tube | 70 kHz | CW | 253692 | ES70-CD, SN 127 | - Simrad WBT Tube | 200 kHz | CW | 253687 | ES200-CDK SN 124 | - Survey settings: Frequency (kHz) | Power | Pulse duration (ms) | Logging Range (m) 38 | 200* | 1.024 | 40 70 | 50* | 1.024 | 40 120 | 50 | 1.024 | 40 200 | 45 | 1.024 | 40 Bio-acoustic system was calibrated using the standard sphere method for survey settings on 14th of November 2018 at the CCS study sitde.. See separated calibration report: Bluefin November 14th 2018 Acoustic calibration report.docx
DOI: 10.25919/51p2-1w18
title: CCS - MV Bluefin 2020 V01 Multibeam Echosounder

description: roject Overview: A range of solutions will be required to reach globally agreed emissions reductions targets for carbon dioxide (CO2). Carbon capture and storage (CCS) is part of the suite of technologies that will contribute to lowering atmospheric emissions of CO2 from Australia's energy system. There are a wide variety of technologies at various stages of technical and commercial readiness, with more development underway for cost effective CO2 capture and storage. Our research will provide new knowledge to inform cost-efficient measurement, monitoring and verification (MMV) of the environment of CCS projects in coastal waters. --o-- Kongsberg EM2040c Multibeam Echosounder – This multibeam echosounder is a bathymetric echosounder that has a frequency range of 200kHz- 400kHz. Two transducers were used mounted at 30° to increase across track coverage, commonly known as a dual head setup. The data was collected in dual swath mode to increase the alongtrack resolution. Oceans and Atmosphere. Relevant component details: make, model, serial number, firmware version, settings: Kongsberg EM2040C Head 2, S/N: 113, 300kHz;Continuouse Wave; High Density Equidistant;Dual Head; Dual Swath Kongsberg EM2040C Head 1, S/N 1403, 300kHz;Continuouse Wave; High Density Equidistant;Dual Head; Dual Swath 2 x Processing Units, S/N’s 100011 and 20087, Processing unit 100011 was on loan from Geoscience Australia PosMV Positioning and Navigation System, S/N 8438 Valeport Monitor SVP 500, S/N38500, Set to self-record profile Valeport MiniSVS Calibration Information: Calibration of the multibeam echosounder was conducted prior to survey on the transit to site. A second set of calibration lines were run on site for confirmation. Results were applied in post processing. Name(s) and acquisition software used: Multibeam bathymetry, Multibeam backscatter, Multibeam water column acquired using SIS (Seafloor Information System) The resulting products were generated on the GDA2020 datum and reduced to the Australian Height Datum using AusGeoid2020 model.
DOI: 10.1285/i9788883051715pi
title: Copertina
DOI: 10.25919/8rwb-3611
title: Multiplex PCR data of Australian Lungfish, Murray River Cod, and Mary River Cod

description: Multiplex PCR data of age associated CpG sites to predict age in Australian Lungfish (Neoceratodus forsteri), Murray River Cod (Maccullochella peelii), and Mary River Cod (Maccullochella mariensis).
DOI: 10.1285/i9788883051715p1
title: Frontespizio e pagine iniziali
DOI: 10.1285/i9788883051715p3
title: Indice
DOI: 10.1285/i9788883051715p5
title: Prefazione
DOI: 10.1285/i9788883051715p9
title: Razze e culture: percorsi e presupposti di uno slittamento semantico e della sua incidenza pratica
DOI: 10.1285/i9788883051715p25
title: L'antisemitismo novecentesco e la nascita delle fake news
DOI: 10.5523/bris.sthhydmnogk72vue56b51uftg
title: Data from Murphy et al. 2021. Empirical distributions of homoplasy in morphological data. Palaeontology

description: This dataset contains analytic code, cladistic datasets and metadata derived from analysis of these datasets, as well as a description of data sources. The data accompanies the following manuscript: Jodie L. Murphy, Mark N. Puttick, Joseph E. O’Reilly, Davide Pisani and Philip C. J. Donoghue. 2021. Empirical distributions of homoplasy in morphological data. Palaeontology.
DOI: 10.1285/i9788883051715p35
title: Con Socrate anche Shahrazàd: la cultura dell'altro e l'arricchimento narrante delle ragioni del non morire
DOI: 10.1285/i9788883051715p43
title: Forestieri in patria alle origini del cristianesimo
DOI: 10.25919/bx96-k533
title: CCS - MV Bluefin 2018 V01 Bioacoustics

description: This bio-acoustic data was collected during a May 2018 moorings deployment voyage on the Australian Maritime College training vessel MV Bluefin as part of the Carbon Capture and Storage project. The survey was conducted in the nearshore of the Gippsland region. See voyage report . Two frequency bio-acoustic water column and seafloor backscatter. Bluefin voyages in December 2017 and November 2018 had a four frequency echosounder system, two installed echosounders at 38 and 120 kHz and two temporary echosounders at 70 and 200 kHz. During this voyage just the installed 38 and 120 kHz echosounders were used . Data was acquired using vessel’s simrad EK60 software. Acosutic data were recorded in UTC time. Local time was UTC + 10. Echosounder details: Echosounder | Frequency | Mode | Transceiver Serial no | Transducer Model & serial No | Comments Simrad EK60 | 38 kHz | CW | - | ES38B, SN30333 | Using CSIRO EK60 GPT on loan as MV Bluefin GPT was faulty. Simrad EK60 | 120 kHz | CW | - | ES120-7C, 120 | - Survey settings: Frequency (kHz) | Power | Pulse duration (ms) | Logging Range (m) 38 | 2000 | 1.024 | 250 120 | 500 | 1.024 | 250 Bio-acoustic system was calibrated using the standard sphere method for survey settings on 6th December 2017 and again on the 14th of November 2018 at the CCS study sitde.. See separated calibration report
DOI: 10.1285/i9788883051715p49
title: Dall'Africa al Nuovo mondo: la tratta atlantica raccontata dagli schiavi
DOI: 10.1285/i9788883051715p59
title: L'altro, lo straniero, il nemico. Politiche e governance delle migrazioni tra razzismo e discriminazione. Il caso dell'Italia nel panorama europeo.
DOI: 10.1285/i9788883051715p69
title: Paradigmi della razza. Evoluzione e degenerazione nel razzismo Contemporaneo
DOI: 10.1285/i9788883051715p77
title: Il fattore umano del razzismo digitale
DOI: 10.1285/i9788883051715p91
title: La possibilità occulta insita nella nostra era: l'Olocausto per Zygmunt Bauman
DOI: 10.1285/i9788883051715p95
title: Hate speech: un profilo linguistico
DOI: 10.1285/i9788883051715p105
title: Le migrazioni e la crescita economica e le fallacie del nativismo
DOI: 10.21227/fm87-mm56
title: SDU-Haier-ND: A dataset for noise detection

description: SDU-Haier-ND (Shandong University-Haier-Noise Detection) is a sound dataset jointly constructed by Shandong University and Haier, which contains the operating sound of the internal air conditioner collected during the product quality inspection. We collected and marked a batch of quality inspection sounds of air conditioners in real production environments to form this data set, including normal sound samples and abnormal sound samples. The data set contains a variety of abnormal sounds, covering a variety of common failures of air conditioners, such as abnormalities caused by bearings or guide plates.AcknowledgementThis work is supported by the Major Scientific and Technological Innovation Project of Shandong Province "Application Demonstration of A New Intelligent Manufacturing Control Platform with Cloud-Edge Collaboration Mechanism". The research team of Shandong University cooperated with Haier Jiaozhou Air-conditioner Interconnected Factory build the dataset for appearance quality detection. If you have any questions or suggestions, do not hesitate to contact the corresponding author: Prof. Dongfeng Yuan, dfyuan@sdu.edu.cn.
DOI: 10.5281/zenodo.4629401
title: Revenue and expenses Civil Service Vol. To 2019

description: <pre>Revenue and expenses Civil Service Vol. To 2019</pre>
DOI: 10.6084/m9.figshare.13643387
title: Metadata record for: EORNA, a barley gene and transcript abundance database.

description: This dataset contains key characteristics about the data described in the Data Descriptor EORNA, a barley gene and transcript abundance database.. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.13643387.v1
title: Metadata record for: EORNA, a barley gene and transcript abundance database.

description: This dataset contains key characteristics about the data described in the Data Descriptor EORNA, a barley gene and transcript abundance database.. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.13486839
title: Metadata record for: NLM-Chem, a new resource for chemical entity recognition in PubMed full text literature

description: This dataset contains key characteristics about the data described in the Data Descriptor NLM-Chem, a new resource for chemical entity recognition in PubMed full text literature. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.13486839.v1
title: Metadata record for: NLM-Chem, a new resource for chemical entity recognition in PubMed full text literature

description: This dataset contains key characteristics about the data described in the Data Descriptor NLM-Chem, a new resource for chemical entity recognition in PubMed full text literature. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.14035613
title: Metadata record for: Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development

description: This dataset contains key characteristics about the data described in the Data Descriptor Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.15128/r1zs25x8480
title: Spatial targeting of nature-based flood risk management measures within river catchments [dataset]

description: The results were generated using the SCIMAP-Flood processer.
description: A wide range of nature-based solutions for flood hazard management work by storing and slowing flow within catchments. There is a need to identify where in a catchment are the optimal locations for the implementation of these solutions. This paper presents a risk-based mapping of the likely locations that contribute to the flood peak and targeting mitigation actions in these locations can be an effective way to reduce damages at points of flood impact downstream. This tool, named SCIMAP-Flood, uses information on land cover, hydrological connectivity, a set of different flood generating rainfall patterns and a set of hydrological travel time distributions to impacted communities. The algorithm then uses this dataset to find the source areas of floodwaters and wrights the important of each location based on its contribution to the flood hazard at each of the downstream points of impact. In the example application, the risk mapping is applied at a 5m grid resolution for the River Eden catchment, Cumbria, England, and hence provide sub-field scale information at the landscape extent. Therefore, the tool can identify sub-catchments where physically based catchment hydrological simulation models can be applied to test different mitigation measure schemes.
description: The results are from SCIMAP-Flood analysis that used geospatial data on rainfall, land cover, hydrological connectivity and water travel times to consider where in the landscape the flood peak water is most likely to have originated from.
DOI: 10.5281/zenodo.4625414
title: Data archive for paper "Machine Learning Emulation of 3D Cloud Radiative Effects"

description: <strong>Overview</strong> This is the data archive for the paper "Machine Learning Emulation of 3D Cloud Radiative Effects". It contains the paper’s data archive with all model outputs (see the <code>results</code> folder) as well as theSingularity image. For the development repository (i.e. only containing source files without generated artefacts), please see https://github.com/dmey/ml-3d-cre. For the Python tool to generate the synthetic data, please refer to the Synthia repository. <strong>Requirements</strong> Linux Singularity &gt;= 3 Portable Batch System (PBS) job scheduler* *Although PBS in not a strict requirement, it is required to run all helper scripts as included in this repository. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the <code>hpc</code> directory (e.g. <code>#PBS -lwalltime=24:00:00</code>). <strong>Initialization</strong> Deflate the data archive with: <pre><code>./init.sh </code></pre> Build the Singularity image with: <pre><code>singularity build --remote tools/singularity/image.sif tools/singularity/image.def </code></pre> Compile ecRad with Singularity: <pre><code>./tools/singularity/compile_ecrad.sh </code></pre> <strong>Usage</strong> To reproduce the results as described in the paper, run the following commands from the <code>hpc</code> folder: <pre><code>qsub -v JOB_NAME=mlp_synthia ./submit_grid_search_synthia.sh qsub -v JOB_NAME=mlp_default ./submit_grid_search_default.sh qsub submit_benchmark.sh </code></pre> then, to plot stats and identify notebooks run: <pre><code>qsub submit_stats.sh </code></pre> <strong>Local development</strong> For local development, notebooks can be run independently. To install the required dependencies, run the following through Anaconda <code>conda env create -f environment.yml</code>. Then, to activate the environment use <code>conda activate radiation</code>. For ecRad, the list of system dependencies are listed in <code>tools\singularity\image.def</code> and can be run with <code>tools\singularity\compile_ecrad.sh</code>. Licence Paper code released under the MIT license. Data released under CC BY 4.0. ecRad released under the Apache 2.0 license.
DOI: 10.6084/m9.figshare.13491165
title: Metadata record for: An updated map of Trypanosoma cruzi histone post-translational modifications

description: This dataset contains key characteristics about the data described in the Data Descriptor An updated map of Trypanosoma cruzi histone post-translational modifications. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.13491165.v1
title: Metadata record for: An updated map of Trypanosoma cruzi histone post-translational modifications

description: This dataset contains key characteristics about the data described in the Data Descriptor An updated map of Trypanosoma cruzi histone post-translational modifications. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.5517/ccdc.csd.cc23r0sp
title: CCDC 1958542: Experimental Crystal Structure Determination

description: Related Article: Suresh Suganya, Ammasai Karthikeyan, Chinnasamy Kalaiarasi, Poomani Kumaradhas|2021|Chem. Data Collect.|33|100670|doi:10.1016/j.cdc.2021.100670
DOI: 10.5517/ccdc.csd.cc23r0tq
title: CCDC 1958543: Experimental Crystal Structure Determination

description: Related Article: Suresh Suganya, Ammasai Karthikeyan, Chinnasamy Kalaiarasi, Poomani Kumaradhas|2021|Chem. Data Collect.|33|100670|doi:10.1016/j.cdc.2021.100670
DOI: 10.5517/ccdc.csd.cc1q597f
title: CCDC 1584014: Experimental Crystal Structure Determination

description: Related Article: Arvind Kumar, Vaskar Sardhalia, Priya Ranjan Sahoo, Ajeet Kumar, Satish Kumar|2021|J.Mol.Struct.|1235|130233|doi:10.1016/j.molstruc.2021.130233
DOI: 10.5517/ccdc.csd.cc1q596d
title: CCDC 1584013: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc25qv6z
title: CCDC 2017951: Experimental Crystal Structure Determination

description: Related Article: Maksym Fizer, Mikhailo Slivka, Vasyl Sidey, Vyacheslav Baumer, Ruslan Mariychuk|2021|J.Mol.Struct.|1235|130227|doi:10.1016/j.molstruc.2021.130227
DOI: 10.5517/ccdc.csd.cc25t0df
title: CCDC 2020034: Experimental Crystal Structure Determination

description: Related Article: Francisco Sánchez-Férez, Teresa Calvet, Mercè Font-Bardia, Josefina Pons|2021|J.Mol.Struct.|1235|130219|doi:10.1016/j.molstruc.2021.130219
DOI: 10.5517/ccdc.csd.cc25t0fg
title: CCDC 2020035: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc26b1zl
title: CCDC 2035459: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc26c3yn
title: CCDC 2036481: Experimental Crystal Structure Determination

description: Related Article: Muhammad Ashfaq, Georgii Bogdanov, Akbar Ali, Muhammad Nawaz Tahir, Safia Abdullah|2021|J.Mol.Struct.|1235|130215|doi:10.1016/j.molstruc.2021.130215
DOI: 10.5517/ccdc.csd.cc26qlm5
title: CCDC 2047507: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc21swzq
title: CCDC 1900764: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc26v3pw
title: CCDC 2050888: Experimental Crystal Structure Determination

description: Related Article: Awatef Selmi, Rihab AYDI, Omar Kammoun, Hajer Bougatef, Ali Bougatef, Nabil MILED, Othman A. Alghamdi, Majed KAMMOUN|2021|J.Mol.Struct.|1235|130230|doi:10.1016/j.molstruc.2021.130230
DOI: 10.5517/ccdc.csd.cc26yspn
title: CCDC 2054422: Experimental Crystal Structure Determination

description: Related Article: Jia-Qi Li, Rui-Ning Li, Ming-Xing Li, Min Shao, Xiang He|2021|J.Solid State Chem.|298|122110|doi:10.1016/j.jssc.2021.122110
DOI: 10.5517/ccdc.csd.cc26ysrq
title: CCDC 2054424: Experimental Crystal Structure Determination

description: Related Article: Jia-Qi Li, Rui-Ning Li, Ming-Xing Li, Min Shao, Xiang He|2021|J.Solid State Chem.|298|122110|doi:10.1016/j.jssc.2021.122110
DOI: 10.5517/ccdc.csd.cc26zxpt
title: CCDC 2055507: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc26zxqv
title: CCDC 2055508: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc26zxrw
title: CCDC 2055509: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc26zxsx
title: CCDC 2055510: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc26zxty
title: CCDC 2055511: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc26ynkd
title: CCDC 2054294: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc26zxvz
title: CCDC 2055512: Experimental Crystal Structure Determination

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DOI: 10.6084/m9.figshare.13999484
title: Metadata record for: AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19

description: This dataset contains key characteristics about the data described in the Data Descriptor AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.13999484.v1
title: Metadata record for: AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19

description: This dataset contains key characteristics about the data described in the Data Descriptor AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.13503114
title: Metadata record for: Downscaling GRACE total water storage change using partial least squares regression

description: This dataset contains key characteristics about the data described in the Data Descriptor Downscaling GRACE total water storage change using partial least squares regression. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.13503114.v1
title: Metadata record for: Downscaling GRACE total water storage change using partial least squares regression

description: This dataset contains key characteristics about the data described in the Data Descriptor Downscaling GRACE total water storage change using partial least squares regression. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.1285/i9788883051715p115
title: Razzismo senza razza: manipolazione dell'Alterità ed esclusione nel mondo greco
DOI: 10.1285/i9788883051715p129
title: Schiuma della terra. Una riflessione sulla vulnerabilità dei rifugiati a partire da Arendt e Weil
DOI: 10.1285/i9788883051715p139
title: Amico, nemico. Risorse culturali per lo sviluppo sociale