cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7794524
title: Extending the application of connectivity metrics within the framework of the characterization of the dynamic behaviour of a WDS subjected to users' activity

description: Water distribution networks (WDNs) are complex combinations of nodes and links and for their analysis several approaches based on graph theory have recently been proposed, mainly considering steady-state flow conditions. However, in their real functioning, WDNs are continuously subjected to pressure transients generated by users’ activity and manoeuvres on regulation devices. This study investigates the application of some metrics from graph theory, already used in the context of network steady-state analyses, and proposes a new one for assessing the effects of changes in the topological structure of a network, due for example to sectorization or branching operations, on the dynamic response of the network itself subjected to users’ activity. The analyses show that connectivity metrics, and in particular the metric introduced here, can reflect the dynamic pressure behaviour of the network and thus provide useful indications for design and management operations, taking unsteady aspects into account.
DOI: 10.5281/zenodo.8172321
title: Extending the application of connectivity metrics within the framework of the characterization of the dynamic behaviour of a WDS subjected to users' activity

description: Water distribution networks (WDNs) are complex combinations of nodes and links, and the current tendency is to modify their topological structure through the closure of isolation valves for monitoring and water quality reasons. For their analysis, several approaches based on graph theory have recently been proposed, mainly considering steady-state flow conditions. However, in their real functioning, WDNs are continuously subjected to pressure transients generated by manoeuvres on regulation devices or by users’ activity. This study investigates the application of some metrics from graph theory, already used in the context of steady-state analysis, for assessing the effects of changes in the topological structure of a network ‒ due for example to sectorization or branching operations ‒ on its transient response when subjected to manoeuvres on devices such as hydrants, pumps, etc. or users’ activity. The analysis shows that some connectivity metrics can effectively reflect the dynamic pressure behaviour of the network and, thus, provide useful indications for design and management operations taking into account unsteady flow features.
DOI: 10.5255/ukda-sn-853359
title: Poverty alleviation in the wake of typhoon Yolanda: Survey data 2015-2017
DOI: 10.5287/bodleian:kzoqa4nk8
title: Supporting data for the paper "Efficient white noise sampling and coupling for multilevel Monte Carlo with non-nested meshes"
DOI: 10.21227/0z4m-jj69
title: SRS_dataset_Collietal_2018

description: The dataset is supporting material for Colli et al 2018 paper. The paper describes the results obtained by the application of an innovative environmental monitoring technique able to estimate rainfall intensity in real-time by processing the attenuation of microwave satellite link signal measured by low cost sensors.  The satellite used during our work, Turksat 42° E, belong to the plethora of satellites operating for television and radio channel broadcasting. Each sensor exploits off the shelf components, is equipped with a radio frequency power-measuring unit, and provides connectivity to the server over a Wide Area network. A validation of the approach with a field comparison experiment at the urban scale, comprising three measurement sites equipped with such sensors, was established since autumn 2016 in the municipality of Genoa (Italy). Point-scale rainfall intensity measurements made by two calibrated tipping-bucket rain gauges constitute the reference for the comparative analysis of the microwave sensors performance. The dynamic calibration of the rain gauges was carried out by using an automatic calibration rig and the measurements have been processed with advanced algorithms to reduce counting errors. The experimental set-up allowed a full characterization of the microwave signal trends as a function of different precipitation. The results showed a strong correlation between the microwave signal attenuation and the reference rainfall observations and demonstrated the possibility to retrieve ten-minute rain accumulations from the microwave links by adopting a proper electromagnetic model. The comparison between the different measuring systems is performed by computing the time series statistics and the frequency of the rain conditions for each precipitation event.
DOI: 10.17632/tjyx29yjgh.1
title: Data for: Tick species diversity and population dynamics of Ixodes ricinus in Galicia (NW Spain)

description: Supplementary Table 1. Includes data on the molecular characterization of tick isolates. Supplementary Table 2. Study 1 includes raw data on the number of ticks considering species and stage collected in every sampling site (NW Spain). Supplementary Table 3. Study 2 includes raw data on the number of ticks considering species and stage monthly collected in three diferent sampling areas during two complete years. Data used for GLMN analysis was also included.
DOI: 10.5281/zenodo.1434786
title: Rir Samples Darmstadt And Helsinki, Summer-Autumn 2018

description: This dataset contains Room Impulse Response (RIR) measurements that we collected for evaluating the DoubleEcho proximity verification system. The package contains the original RIR sample recordings, processed audio files after deconvolution with the original signal and cropping the RIR, and precomputed features in CSV files. All the audio files are 16bit mono PCM Wave files.<br>
<br>
The original.wav file is the generated excitation signal that was emitted in the rooms.<br>
Other files follow the naming convention recorded_ROOM_emitter_EMITTER-ID_RECORDER-ID_TIMESTAMP{_cutrir}.wav<br>
The files where .wav follows directly the timestamp are the original recordings; the ones with <em>cutrir</em> in their name are the cropped RIR samples.

The CSV files contain extracted features like the RT60 value for the entire signal and for specific frequency ranges. An example of the extracted features of a single sample:

<pre><code>Filter	Band	RT60	EDT	D/R	C10	C35	C50	C80
		0.566	0.401	9.544	-0.590	5.064	7.300	11.096
Oct	125Hz	0.093	1.195	9.548	-9.351	-4.757	-2.745	-1.001
Oct	250Hz	0.430	3.367	9.552	-11.705	-8.236	-6.218	-4.841
Oct	500Hz	0.692	0.809	9.564	-3.952	-0.989	0.377	4.227
Oct	1kHz	0.640	0.582	9.559	-1.836	1.438	3.581	7.143
Oct	2kHz	0.636	0.484	9.543	-0.357	3.971	5.744	9.415
Oct	4kHz	0.589	0.370	9.549	0.451	6.333	8.095	11.789
Oct	8kHz	0.518	0.398	9.545	-1.018	4.701	7.201	11.088
Oct	16kHz	0.471	0.318	9.548	-1.463	5.970	8.914	13.408
1/3	125Hz	0.042	0.415	9.605	-2.504	-0.749	0.129	22.719
1/3	160Hz	0.122	0.633	9.573	-2.150	3.079	4.293	4.603
1/3	200Hz	0.274	2.361	9.545	-13.815	-9.676	-8.875	-6.187
1/3	250Hz	0.156	2.175	9.554	-9.833	-6.198	-5.467	-5.086
1/3	315Hz	0.046	0.572	9.557	-4.743	-2.653	3.075	7.797
1/3	400Hz	0.184	0.859	9.545	-6.032	-0.763	3.208	4.707
1/3	500Hz	0.688	0.976	9.555	-4.107	-0.176	1.401	2.865
1/3	630Hz	0.664	0.805	9.558	-6.423	-2.636	-1.604	3.992
1/3	800Hz	0.672	0.771	9.546	-3.799	-1.284	0.163	4.211
1/3	1kHz	0.620	0.644	9.546	-3.718	-0.355	0.606	4.326
1/3	1.3kHz	0.632	0.566	9.551	-3.745	0.293	3.464	8.282
1/3	1.6kHz	0.704	0.520	9.550	-1.082	3.188	5.015	7.854
1/3	2kHz	0.643	0.518	9.554	-0.331	2.060	5.214	8.615
1/3	2.5kHz	0.643	0.516	9.568	-1.302	4.395	5.647	8.990
1/3	3.2kHz	0.589	0.456	9.546	0.773	4.569	5.872	10.156
1/3	4kHz	0.586	0.351	9.549	-0.148	7.023	8.817	12.605
1/3	5kHz	0.603	0.435	9.545	-0.709	5.325	7.042	10.348
1/3	6.3kHz	0.567	0.385	9.546	-2.436	4.609	6.797	11.164
1/3	8kHz	0.502	0.414	9.545	-0.812	4.478	7.015	10.773
1/3	10kHz	0.438	0.328	9.554	-1.899	5.855	8.598	13.376
1/3	12.5kHz	0.321	0.237	9.598	-1.060	8.030	12.542	17.905
1/3	16kHz	0.541	0.341	9.546	0.635	6.372	8.562	11.948
1/3	20kHz	0.539	0.205	9.587	1.624	9.700	11.512	13.696</code></pre>
DOI: 10.17031/x3xoek
title: 2010-ongoing UK Offshore Marine Conservation Zone (MCZ) Survey Data

description: 2010-ongoing UK Offshore Marine Conservation Zone (MCZ) Survey Data
DOI: 10.26300/0dtq-hf36
title: Adjudicated/Annotated Telemetry Signals for Medically Important and Clinically Significant Events-3 (ATOMICS-3) Dataset
DOI: 10.25919/5baae2e9c6d80
title: Parkes observations for project P892 semester 2018APRS_BPSR_50

description: SUPERBx looks at the highest Galactic latitudes in a search for pulsars and fast radio bursts (FRBs). We use optimised GPU codes and supercomputing to search for these, making discoveries in real time. Handling our data as it comes in is essential for the SKA Phase I era so this work applies directly to the high-data rates of next generation telescopes. Our pulsar discoveries are typically nearby scintillating sources, intermittent sources and those in interesting binary systems. Our FRB discoveries have discovery lags of ~1 second, rather than months/years and these feed rapid alerts to the world-wide follow-up community via a dedicated VOEvent format an Astronomer's Telegrams. The goal of this is to allow localisation of the discovered FRBs. This is key for identifying FRB host galaxies, so as to solve the mystery of their progenitors and to exploit their many uses as tools for precision cosmology measurements.
DOI: 10.5281/zenodo.1434061
title: Ev Data Monitoring

description: Electric Vehicle (EV) data collected by Emotion, expert Italian company specialized in the sector of electric mobility, partner of SOFIE project.

These data were collected using a particular OBD device connected on each EV. OBD communicates using a TCP/IP system.

Data shown in this dataset were updated every 10 seconds.
DOI: 10.5281/zenodo.1434062
title: Ev Data Monitoring

description: Electric Vehicle (EV) data collected by Emotion, expert Italian company specialized in the sector of electric mobility, partner of SOFIE project.

These data were collected using a particular OBD device connected on each EV. OBD communicates using a TCP/IP system.

Data shown in this dataset were updated every 10 seconds.
DOI: 10.5281/zenodo.1436384
title: Metagenomic Datasets Gutless Oligochetes (2018)

description: This list contains accession numbers to:

-Metatranscriptomic data of gutless oligochaete Olavius algarvensisworms depositied in European Nucleotide Archive ENA

-Metaproteomic data of gutless oligochaete Olavius algarvensisworms deposited in proteomics data repositories

-Draft genomes of endosymbionts from various gutless oligochaete hosts, in submission process.

If you are interested in these datasets, please let us know.
DOI: 10.5281/zenodo.1436385
title: Metagenomic Datasets Gutless Oligochetes (2018)

description: This list contains accession numbers to:

-Metatranscriptomic data of gutless oligochaete Olavius algarvensisworms depositied in European Nucleotide Archive ENA

-Metaproteomic data of gutless oligochaete Olavius algarvensisworms deposited in proteomics data repositories

-Draft genomes of endosymbionts from various gutless oligochaete hosts, in submission process.

If you are interested in these datasets, please let us know.
DOI: 10.5061/dryad.5hq26p1/1
title: Pollinator visitation data

description: Raw pollinator visitation data for the N = 65 plant species studied.
DOI: 10.5061/dryad.5hq26p1/2
title: Characteristics of individual plant species

description: Sampling site elevation, life form, and corolla type, for individual plant species
DOI: 10.5061/dryad.5hq26p1/3
title: R Script 1

description: # Script 1
# TESTS #1 AND #2 IN TABLE 2
# Section "Individual plant species: patch visitation"
DOI: 10.5061/dryad.5hq26p1/4
title: R Script 2

description: # Script 2 
# TESTS #3 AND #4 IN TABLE 2
# Section "Individual plant species: flower visitation"
DOI: 10.5061/dryad.5hq26p1/5
title: R Script 3

description: # Script 3
# 
# Section "Visitation probabilities and flowers per patch"
DOI: 10.5061/dryad.5hq26p1/6
title: R Script 4

description: # Script 4
# 
# Section "Supra-annual fluctuations in pollinator composition"
DOI: 10.5281/zenodo.1435734
title: Maja Look-Up Tables For Venµs Satellite, For A Continental Aerosol Model

description: This archive contains the look-up tables used by MAJA atmospheric correction software, used to process Venµs satellite data. These look-up tables correspond to a continental model.

V1.1 corrects for a bug in VE_TEST_GIP_L2DIRT_L_ALLSITES_00010_20000101_99991231.HDR, (Direct Transmission file) which contained references to Diffuse transmission files for bands 7 to 12.
DOI: 10.5061/dryad.5hq26p1/7
title: R Script 5

description: # Script 5
# TESTS #5 AND #8 IN TABLE 2
# Section "Community-level trends: all pollinators"
DOI: 10.5061/dryad.5hq26p1/8
title: R Script 6

description: # Script 6
# TESTS #6 AND #9 IN TABLE 2
# Section "Community-level trends: insect orders"
DOI: 10.5061/dryad.5hq26p1/9
title: R Script 7

description: # Script 7
# TESTS #7 AND #10 IN TABLE 2
# Section "Community-level trends: selected genera"
DOI: 10.5281/zenodo.1434204
title: Aligned Dna Sequence Matrix For Phylogenetic Analyses In The Article "A New Species Of Fossorial Salamander Of The Genus Oedipina (Plethodontidae) From The Northwestern Ecuador"

description: Aligned DNA sequence matrix for phylogenetic analyses of the article "". The matrix is in NEXUS format.

Gene partition as arranged as follows (tRNAs are included as part of larger adjacent genes):

16S rRNA, tRNA<sup>Leu</sup>, ND1, tRNA<sup>Ile</sup> and tRNA<sup>Gln</sup>: 1-1890

Cytochrome B = 1891-2275
DOI: 10.5523/bris.3sem1fxxygqod2n8bh1tbt6x16
title: Enhancing the dynamic performance of a pantograph-catenary system via inerter-based damping technology

description: The manuscript, pictures and codes in this paper are offered.
DOI: 10.7910/dvn/pgicdm/rso4be
title: README
DOI: 10.17632/dtdgkw3z5z.1
title: Data for: The Significance of Maars for Palaeoclimatic Study in China

description: Table 1- The location of maar lakes with palaeoclimate records Table 2- The maar lakes with varves Table 3- The maar lake with LCA and GDGTs
DOI: 10.5281/zenodo.1434894
title: Data And Script For Laass Et. Al "Characterization Of The Transcriptome Of Haloferax Volcanii With Mixed Rna-Seq"

description: Data and script to accompany the manuscript "Characterization of the transcriptome of Haloferax volcanii with mixed RNA-Seq".
DOI: 10.5281/zenodo.1435421
title: Magnaporthe Oryzae Polyadenylation Sites For Wild-Type And Delta-Rbp35 Mutant

description: Magnaporthe oryzae polyadenylation sites for wild-type and delta-rbp35 mutant, in 4 media: CM, MM, MM-N and MM-C
DOI: 10.7487/2018.267.1.1144
title: Sam Simpson UKHO

description: Total biolum plankton taxa,all north atlantic, 1958-2016
DOI: 10.5281/zenodo.1434893
title: Data And Script For Laass Et. Al "Characterization Of The Transcriptome Of Haloferax Volcanii With Mixed Rna-Seq"

description: Data and script to accompany the manuscript "Characterization of the transcriptome of Haloferax volcanii with mixed RNA-Seq".
DOI: 10.5281/zenodo.1435242
title: Data For "Multi-Instrument Studies Of Thermospheric Weather Above Alaska"

description: These files contain the original data used to generate figures presented in "Multi-instrument studies of thermospheric weather above Alaska" submitted to the Journal of Geophysical Research - Space Physics in 2018 by authors M. G. Conde, W. A. Bristow, D. L. Hampton, and J. Elliott.

The main figures in this work were generated by the IDL program named "sdi3k_newmapper.pro", which has also been uploaded here.

Data providers were:

Scanning Doppler Imager data (files with names ending in "NZ0115": M. G. Conde, University of Alaska Fairbanks

SuperDARN ion convection (".map.vec.geo" or ".geo.dat" files): W. A. Bristow, University of Alaska Fairbanks

PFISR ion convection (.h5 files): Roger Varney, SRI International

SSUSI auroral images (".nc"files with the word "SSUSI" in the filename): Larry Paxton, John's Hopkins University Applied Physics Lab

All-sky camera images (".FIT" or ".jpg" files): D. L. Hampton, University of Alaska Fairbanks

Magnetometer data (".csv files): D. L. Hampton, University of Alaska Fairbanks
DOI: 10.5281/zenodo.1435243
title: Data For "Multi-Instrument Studies Of Thermospheric Weather Above Alaska"

description: These files contain the original data used to generate figures presented in "Multi-instrument studies of thermospheric weather above Alaska" submitted to the Journal of Geophysical Research - Space Physics in 2018 by authors M. G. Conde, W. A. Bristow, D. L. Hampton, and J. Elliott.

The main figures in this work were generated by the IDL program named "sdi3k_newmapper.pro", which has also been uploaded here.

Data providers were:

Scanning Doppler Imager data (files with names ending in "NZ0115": M. G. Conde, University of Alaska Fairbanks

SuperDARN ion convection (".map.vec.geo" or ".geo.dat" files): W. A. Bristow, University of Alaska Fairbanks

PFISR ion convection (.h5 files): Roger Varney, SRI International

SSUSI auroral images (".nc"files with the word "SSUSI" in the filename): Larry Paxton, John's Hopkins University Applied Physics Lab

All-sky camera images (".FIT" or ".jpg" files): D. L. Hampton, University of Alaska Fairbanks

Magnetometer data (".csv files): D. L. Hampton, University of Alaska Fairbanks
DOI: 10.25781/kaust-3804z
title: Thesis Chapter 3 tables

description: The dataset includes the IDs, phenotypic measurements, multivariate analysis and genome-wide association data collected from the Arabidopsis HapMap population that was used in a high-throughput phenotyping experiment under salt stress to identify the underlying genetic components.
DOI: 10.5281/zenodo.1434285
title: SOTorrent Dataset

description: Stack Overflow (SO) is the most popular question-and-answer website for software developers, providing a large amount of code snippets and free-form text on a wide variety of topics. Like other software artifacts, questions and answers on SO evolve over time, for example when bugs in code snippets are fixed, code is updated to work with a more recent library version, or text surrounding a code snippet is edited for clarity. To be able to analyze how content on SO evolves, we built <em>SOTorrent</em>, an open dataset based on the official SO data dump. <em>SOTorrent </em>provides access to the version history of SO content at the level of whole posts and individual text or code blocks. It connects SO posts to other platforms by aggregating URLs from text blocks and comments, and by collecting references from GitHub files to SO posts. Our vision is that researchers will use <em>SOTorrent </em>to investigate and understand the evolution of SO posts and their relation to other platforms such as GitHub.

<strong>If you use this dataset in your work, please cite our MSR 2018 paper (BibTex) or our MSR 2019 mining challenge proposal.</strong>
description: The dataset is based on the official Stack Overflow data dump released 2018-09-05 (https://archive.org/details/stackexchange) and the Google BigQuery GitHub data set queried 2018-09-23 (https://cloud.google.com/bigquery/public-data/github). Please read the license files (LICENSE.md) before using the dataset.
DOI: 10.21421/d2/msaxo1
title: Phenotypic data of shoot fly introgression lines in Sorghum for the year 2015 rainy season
DOI: 10.5281/zenodo.1422786
title: Netherlands F3 Interpretation Dataset

description: <strong>Netherlands F3 Interpretation Dataset</strong>

Machine learning and, more specifically, deep learning algorithms have seen remarkable growth in their popularity and usefulness in the last years. Such a fact is arguably due to three main factors: powerful computers, new techniques to train deeper networks and more massive datasets. Although the first two are readily available in modern computers and ML libraries, the last one remains a challenge for many domains. It is a fact that big data is a reality in almost all fields today, and geosciences are not an exception. However, to achieve the success of general-purpose applications such as ImageNet - for which there are +14 million labeled images for 1000 target classes - we not only need more data, we need more high-quality labeled data. Such demand is even more difficult when it comes to the Oil & Gas industry, in which confidentiality and commercial interests often hinder the sharing of datasets to others. In this letter, we present the Netherlands interpretation dataset, a contribution to the development of machine learning in seismic interpretation. The Netherlands F3 dataset was acquired in the North Sea, offshore Netherlands. The data is publicly available and comprises pos-stack data, eight horizons and well logs of 4 wells. However, for the dataset to be of practical use for our tasks, we had to reinterpret the seismic, generating nine horizons separating different seismic facies intervals. The interpreted horizons were used to create 651 labeled masks for inlines and 951 for crosslines. We present the results of two experiments to demonstrate the utility of our dataset. 

<strong>Dataset contents</strong>


	Crosslines:
	
		Classes: 10
		Number of slices: 651
		Records per class: 9,440
		Total of records: 94,400
	
	
	Inlines:
	
		Classes: 10
		Number of slices: 951
		Records per class: 9,720
		
			Total of records: 94,720
		
		
	
	
	Configuration:
	
		Crop: [0, 0, 0, 0]
		Gray levels: 256
		Noise: 0.3
		Percentile: 5.0
		Strides: [20, 48]
		Tile shape: [25, 64, 1]
DOI: 10.5281/zenodo.1422787
title: Netherlands F3 Interpretation Dataset

description: <strong>Netherlands F3 Interpretation Dataset</strong>

Machine learning and, more specifically, deep learning algorithms have seen remarkable growth in their popularity and usefulness in the last years. Such a fact is arguably due to three main factors: powerful computers, new techniques to train deeper networks and more massive datasets. Although the first two are readily available in modern computers and ML libraries, the last one remains a challenge for many domains. It is a fact that big data is a reality in almost all fields today, and geosciences are not an exception. However, to achieve the success of general-purpose applications such as ImageNet - for which there are +14 million labeled images for 1000 target classes - we not only need more data, we need more high-quality labeled data. Such demand is even more difficult when it comes to the Oil & Gas industry, in which confidentiality and commercial interests often hinder the sharing of datasets to others. In this letter, we present the Netherlands interpretation dataset, a contribution to the development of machine learning in seismic interpretation. The Netherlands F3 dataset was acquired in the North Sea, offshore Netherlands. The data is publicly available and comprises pos-stack data, eight horizons and well logs of 4 wells. However, for the dataset to be of practical use for our tasks, we had to reinterpret the seismic, generating nine horizons separating different seismic facies intervals. The interpreted horizons were used to create 601 labeled masks for inlines and 482 for crosslines. We present the results of two experiments to demonstrate the utility of our dataset. 

<strong>Dataset contents</strong>


	Crosslines:
	
		Classes: 9 
		Number of slices: 482
		Records per class: 864
		Total of records: 7,776
	
	
	Inlines:
	
		Classes: 9
		Number of slices: 601
		Records per class: 2,994
		Total of records: 26,496
	
	
	Configuration
	
		Crop: [0, 0, 0, 0]
		Gray levels: 256
		Noise: 0.3
		Percentile: 5.0
		Strides: [20, 20]
		Tile shape: [40, 40, 1]