cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4121/14405783.v1
title: Data Supplement: Fracture Subgraphs from the Lilstock Pavement, Bristol Channel, UK

description: This dataset is supplement to the manuscript, "Investigating Spatial Heterogeneity within Fracture Networks using<br>Hierarchical Clustering and Graph Distance Metrics". The dataset consists of circular subgraphs that are sampled from larger 2D fracture networks using a fixed spacing and diameter. The large-scale fracture networks in the form of spatial graphs can be found in the dataset, "Fracture Patterns from the Lilstock Pavement, Bristol Channel, UK (https://doi.org/10.4121/14039234)". <br>The subgraphs are extracted from fracture networks corresponding to three regions that are named as Regions 1,2, and 3. Region 1 contains 219 subgraphs (spread over approximately 6017 sq. m), Region 2 contains 212 subgraphs (spread over approximately 6749 sq. m), and Region 3 contains 117 subgraphs (spread over approximately 1473 sq. m). The subgraphs are sampled with diameters of 7.5 m. The spacing between sampling circle centers is 5 m for Regions 1 and 2, and is 3 m for Region 3, so that there is some degree of overlap between the subgraphs. <br>The subgraph data is in the form of *.mat files. Each subgraph sample consists of a MATLAB graph object and a positioning matrix. The positioning is relative and may be georeferenced to UTM Zone 30 N or Coordinate Reference System EPSG: 32630 by shifting origin of the spatial positioning matrices using the 'xmin','ymin' variables corresponding to each region.
DOI: 10.5281/zenodo.3793090
title: Model Counting and Uniform Sampling Instances

description: These instances mainly consist of the formulas that have been used in the evaluation of recent model counting techniques. A significant set of benchmarks involving sampling set, i.e., they are meant for projected model counting.<br> <br> The specification for reading such files can be found at https://github.com/meelgroup/approxmc<br> <br> Here is list of some of the papers that have reported results on these instances: 1. BIRD: Engineering an Efficient CNF-XOR SAT Solver and its Applications to Approximate Model Counting<br> Mate Soos and Kuldeep S. Meel<br> Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2019. 2. Accelerating Approximate Techniques for Counting and Sampling Models Through Refined CNF-XOR Solving<br> Mate Soos, Stephan Gocht, and Kuldeep S. Meel<br> Proceedings of International Conference on Computer-Aided Verification (CAV), 2020.<br>
DOI: 10.5281/zenodo.6875264
title: Data and code for Scale-invariant random geometry from mating of trees: a numerical study

description: This entry contains the source code, simulation data and data analysis notebooks for the paper T. Budd, A. Castro, "Scale-invariant random geometry from mating of trees: a numerical study." arXiv:2207.05355. This work is part of START-UP 2018 programme with project number 740.018.017, which is financed by the Dutch Research Council (NWO).
DOI: 10.5287/bodleian:0z9mybmyz
title: Full Excel model: Life-cycle environmental impacts of food & drink products
DOI: 10.7924/r4dr2z58t
title: Data from: Improving SABRE hyperpolarization with highly non-intuitive pulse sequences: moving beyond avoided crossings to describe dynamics

description: Signal Amplification By Reversible Exchange (SABRE) creates "hyperpolarization" (large spin magnetization) using a transition metal catalyst and parahydrogen, addressing the sensitivity limitations of magnetic resonance. SABRE and its heteronuclear variant X-SABRE are simple, fast and general, but to date have not produced polarization levels as large as more established methods. We show here that inaccuracies in the theoretical framework for these applications, which focuses on avoided crossings (also called level anti-crossings or LACs) steer current SABRE and X-SABRE experiments away from optimal solutions. Accurate simulations show astonishingly rich and unexpected dynamics in SABRE/X-SABRE, which we explain with a combination of perturbation theory and average Hamiltonian approaches. This theoretical picture predicts simple pulse sequences with field values far from LACs (both instantaneously and on average), using different terms in the effective Hamiltonian to strategically control evolution and improve polarization transfer. Significant signal enhancements under such highly non-intuitive conditions are verified experimentally.
DOI: 10.17632/hhrb7phdyx.2
title: NADiA: News Articles Dataset in Arabic for Multi-Label Text Categorization

description: NADiA Dataset is the largest, to the best of our knowledge, source for Arabic textual data that can be used in any NLP related task such as text classification. We chose the abbreviation NADiA as it is a common Arabic name. The data was collected by scraping ‘SkyNewsArabia’ and ‘Masrawy’ news websites using Python scripts that are fine-tuned for each website. SkyNewsArabia will be referred to as NADiA1, while the latter would be NADiA2. NADiA1 is a big dataset containing 37,445 files, while NADiA2 is a huge dataset that contains 678,563 files. However, after filtering and cleaning we reduced the numbers to 35,416 and 451,230 for NADiA 1 and 2, respectively. NADiA1 consists of the following categories (24, displayed in English for easy referencing): News, North Africa, Levant, Middle East, The Americas, Research, Finance & Economy, War & Terrorism, Gulf, Europe, Political Figures, Iran, Technology, Russia, Sports, Tennis, Football, English League, Arabian Sports, Spanish League, Health, East Asia, Environment, Other Countries NADiA2 consists of the following categories (28, displayed in English for easy referencing): Politics, Middle East, Asia, Africa, United States, Europe, Other Countries, Leaders, Sports, Arabian Sports, Football Clubs, Spanish League, Egyptian League, Finance, Arts, Cinema & TV, Fashion, Health, Pregnancy & Delivery, Cancer, Obesity, Social Media, Technology, Religion, Islamic, Fatawa, Worship, Prophet Biography
DOI: 10.5281/zenodo.7244156
title: Privacy-Enhancing Technology and Everyday Augmented Reality: Understanding Bystanders' Varying Needs for Awareness and Consent

description: This data is for our ACM IMWUT 2022 paper "Privacy-Enhancing Technology and Everyday Augmented Reality: Understanding Bystanders' Varying Needs for Awareness and Consent". See the README for more details.
DOI: 10.5281/zenodo.7330950
title: Data: Recent waning snowpack in the Alps is unprecedented in the last six centuries

description: <strong>Data_Dendro.txt</strong> contains the indexed ring-width chronology derived from living and relict common juniper (<em>Juniperus communis</em> L.) collected within or above the treeline in Ventina valley (46° 18’N, 9° 46’E). This data was used to reconstruct snowpack duration from 1400 to 2018 after calibration/validation with snow cover duration modelled from daily temperature and precipitation series. <strong>Data_SALP_Reconstruction.txt</strong> contains the snow cover duration ring-width reconstruction (SALP) spanning from 1400 to 2018. <strong>Data_Snow_cover_duration_south_Alps_1951_2018.txt</strong> contains the gridded 30’’ resolution snow cover duration over southern Alps for the period 1951-2018 as derived from a degree-day model applied to daily temperature and precipitation observations and calibrated on 312 snow depth observational series. This data was used to analyse the spatial signature of the indexed ring-width chronology. <strong>Data_Time_serie_snow_cover_duration_Ventina.txt</strong> contains the snow cover duration series (period 1834-2018) derived for the specific location of the site chronology, obtained with a degree-day model applied to daily temperature and precipitation observations and calibrated on snow depth observational series. These data were used to calibrate/validate the snow cover reconstruction based on the indexed ring-width chronology. They are documented in the following article: Carrer, M., Dibona, R., Prendin, A. L., Brunetti,M., 2022. Recent waning snowpack in the Alps is unprecedented in the last six centuries. Nature Climate Change.
DOI: 10.6084/m9.figshare.14645895.v3
title: Toward Static Test Flakiness Prediction: A Feasibility Study

description: This item contains the replication package related to the construction of a logistic regression model for "Toward Static Test Flakiness Prediction: A Feasibility Study"<br>
DOI: 10.6084/m9.figshare.15164658.v1
title: Archived data for Wenzel et al. Meteoritics and Planetary Science Paper

description: Geochemical data for pyroxene compositions in shergottite NWA 6963<br>
DOI: 10.6084/m9.figshare.17899688.v1
title: Varian et al Manuscript Data and Code-Food web and microenvironment influence vector-borne disease in Panama

description: Data collected for Varian et al. manuscript in review Food web structure and microenvironment affect Chagas disease vector infection and abundance in a rural landscape. Includes data collected from palms (Rhodnius pallescens abundance, T. cruzi infection, blood meals, invertebrate and vertebrate genera/species present in palm crowns, palm crown microhabitat, food web metrics) in rural landscapes in Panama under the following approvals and permits: Mi Ambiente: SE/A-93-13, SE/AP-27-16 STRI IACUC: 2013-0601-2016-02, 2016-0801-2019-A1 UGA IACUC: A2016 05-009-R2 IACUC approval 2013-0601-2016-02; 2016-0801-2019-A1<br><br><br>
DOI: 10.17632/322pzsdxwy.1
title: RTAnews: A Benchmark for Multi-label Arabic Text Categorization

description: RTAnews dataset is a collections of multi-label Arabic texts, collected form Russia Today in Arabic news portal. It consists of 23,837 texts (news articles) distributed over 40 categories, and divided into 15,001 texts for the training and 8,836 texts for the test. The original dataset (without preprocessing), a preprocessed version of the dataset, versions of the dataset in MEKA and Mulan formats, single-label version, and WEAK version all are available. For any enquiry or support regarding the dataset, please feel free to contact us via bassalemi at gmail dot com
DOI: 10.17632/yw2djp4vdg.1
title: Data for: Are the Con Artists Back? Deciphering Social Engineering Attacks

description: Social Engineering Attack Scenarios collected from various sources : News papers, Government advisories, Police education materials and interviews.
DOI: 10.21227/hyzg-9m49
title: Cooking activity dataset with macro and micro activities

description: This dataset features cooking activities with recipes and gestures labeled. The data has been collected using two smartphones (right arm and left hip), two smartwatches (both wrists) and one motion capture system with 29 markers. There were 4 subjects who prepared 3 recipes (sandwich, fruit salad, cereal) 5 times each. The subjects followed a script for each recipe but acted as naturally as possible
DOI: 10.25500/edata.bham.00000855
title: Research data supporting “Optimising Remanufacturing Decision-making using the Bees Algorithm in product digital twins”
DOI: 10.5281/zenodo.4715219
title: Direct sun retrievals of nitrogen dioxide (NO2) total columns from Brewer #067, Rome, Italy (reprocessed with algorithm BNALG2)

description: Cloud-screened and quality-filtered direct sun retrievals of nitrogen dioxide (NO2) vertical column densities (VCDs) derived from MkIV Brewer #067 measurements in Rome (wavelengths 425.02, 431.40, 437.35, 442.83, 448.08, and 453.20 nm) and processed using the Brewer Nitrogen Dioxide Algoritm BNALG2. Calibration is carried out with Bootstrap Estimation techniques. The values represent averages of 5 samples. In the latest version, days with obviously erroneous data (NO2 VCD &gt; 99.9% percentile) have been removed. A detailed description of the method has been accepted as a research article by the ESSD journal (H. Diémoz et al., Advanced NO2 retrieval technique for the Brewer spectrophotometer applied to the 20-year record in Rome, Italy, Earth Syst. Sci. Data, 2021).
description: The Boundary-layer Air Quality-analysis Using Network of Instruments (BAQUNIN) project is funded by ESA (contract ID 4000111304/14/I-AM).
DOI: 10.5281/zenodo.7404918
title: East Asia Reanalysis System (EARS)

description: Reanalysis data plays a vital role in weather and climate study, as well as meteorological resource development and application. In this work, the East Asia Reanalysis System (EARS) was developed using the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolations (GSI) data assimilation system. The regional reanalysis system is forced by the European Centre of Medium-Range Weather Forecasts (ECMWF) global reanalysis EAR-Interim data at 6-h intervals; and hourly surface observations are assimilated by the WRF Four-Dimension Data Assimilation (FDDA) scheme during the model integration; upper observations are assimilated in 3D-VAR mode at analysis moment. The reanalysis runs from 1980 to 2018, producing a regional reanalysis dataset covering East Asia and surrounding areas at 12-km horizontal resolution, 74 sigma levels, and 3-hour intervals. Compared to the global reanalysis data of the EAR-Interim, EARS are closer to the observations in both near-surface and upper-level fields. The present study provides evidence for substantial improvements seen in EARS compared to the ERA-Interim reanalysis fields over East Asia. <strong>Domain</strong>: covering a large domain of an area of 10,800 km x 9,120 km (with 900 x 760 grid points). (left bottom corner 61.6E, 10.3S, right bottom 138.3E, 10.3S, right top 138.3E, 10.3S, and left top 2.51E, 50.8N) The database format is GRIB version 1 and the total volume of the data files is 54.6 TB. The GRIB files are hosted at http://data.cma.cn/ as their total volume exceeds the volume that could be provided by Zenodo. Required files can therefore be downloaded from this location.
description: This study is jointly supported by the National Key Research and Development Program of China (2017YFC1501800), Second Tibetan Plateau Scientific Expedition and Research (STEP) Program (2019QZKK010402), National Natural Science Foundation of China (42075083 and 42192554), and National Department Public Benefit Research Foundation (GYHY201506002).
DOI: 10.6084/m9.figshare.15063063
title: Projected ocean warming constrained by the ocean observational record: supplementary metadata

description: This repository contains data and code used in the following study:<br>Kewei Lyu, Xuebin Zhang, & John A. Church. Projected ocean warming constrained by the ocean observational record. <i>Nature Climate Change</i> (2021), https://doi.org/10.1038/s41558-021-01151-1<br>The following items are included: <br><b>README.txt</b><br><br>% More detailed information<br><br><b>data.mat</b><br>% all required data for main figures<br><br><b>Figure_1.m </b><br>% code to plot three panels of Figure 1<br><br><b>Figure_2.m </b><br>% code to plot five panels of Figure 2<br><b>Figure_3.m </b><br>% code to plot two panels of Figure 3 and to derive constrained projections as reported in this paper based on emergent constraint methodology<br><br><i>Upper-2000m OHC (ZJ): 2081-2100 relative to 2005-2019 under SSP5-8.5</i><i>50% (17-83%) [5-95%]</i><i>------------------</i><i>Original: 1862.6 (1694.7 - 2417.1) [1565.6 - 2594.7]</i><i>Constrained: 1858.6 (1545.7 - 2169.9) [1319.7 - 2391.8]</i><i> </i><i><br></i><i>Upper-2000m thermosteric sea level (cm): 2081-2100 relative to 2005-2019 under SSP5-8.5</i><i>50% (17-83%) [5-95%]</i><i>------------------</i><i>Original: 23.2 ( 21.4 - 30.4) [ 18.9 - 33.1]</i><i>Constrained: 21.7 ( 17.4 - 25.9) [ 14.3 - 28.9]</i><br><b>Figure_3_for_comparison_with_AR6.m</b><br>% similar as Figure_3.m but using Argo record to constrain full-depth projections relative to AR6 baseline period 1995-2014<br><i>Full-depth OHC (ZJ): 2081-2100 relative to 1995-2014 under SSP5-8.5</i><i>50% (17-83%) [5-95%]</i><i>------------------</i><i>Original: 2154.3 (1953.9 - 2567.2) [1759.6 - 2874.7]</i><i>Constrained: 2103.2 (1803.1 - 2401.9) [1581.7 - 2621.8]</i><i>AR6 (Table 9.1): 2230 ( 1920 - 2640) [ 1710 - 3000]</i><i> </i><i><br></i><i>Full-depth thermosteric sea level (cm): 2081-2100 relative to 1995-2014 under SSP5-8.5</i><i>50% (17-83%) [5-95%]</i><i>------------------</i><i>Original: 26.1 ( 23.5 - 32.4) [ 22.0 - 36.1]</i><i>Constrained: 24.4 ( 20.3 - 28.5) [ 17.3 - 31.4]</i><i>AR6 (Table 9.1): 25 ( 20 - 30) [ 18 - 35]</i><br><b>trend.m</b> <br>% code to calculate linear trend (obtained online)<br><br><b>rgb.m</b><b><br></b> % code for colours used in some plots (obtained online)
DOI: 10.6084/m9.figshare.17149208.v10
title: CRISPR/Cas12a-based assay for the rapid and high-sensitivity detection of Streptococcus agalactiae colonization in pregnant women with premature rupture of membrane.xlsx

description: <strong>Background: </strong><em>Streptococcus agalactiae</em> or group B <em>Streptococcus</em> (GBS) is a leading infectious cause of neonatal morbidity and mortality. It is essential to establish a robust method for the rapid and ultra-sensitive detection of GBS in pregnant women with premature rupture of membrane (PROM). <strong>Methods: </strong>This study developed a CRISPR-GBS assay that combined the advantages of the recombinase polymerase amplification (RPA) and CRISPR/Cas12a system for GBS detection. The clinical performance of the CRISPR-GBS assay was assessed using vaginal or cervical swabs that were collected from 179 pregnant women with PROM, compared in parallel to culture-based matrix-assisted laser desorption ionization time-of-flight mass spectrometry (culture-MS) method and real-time quantitative polymerase chain reaction (qPCR) assay. <strong>Results:</strong> The CRISPR-GBS assay can be completed within 35 minutes and the limit of detection was as low as 5 copies μL<sup>-1</sup>. Compared with the culture-MS, the CRISPR-GBS assay demonstrated a sensitivity of 96.64% (144/149, 95% confidence interval [CI] = 92.39–98.56%) and a specificity of 100% (30/30, 95% CI = 88.65–100%). It also had a high concordance rate of 98.88% with the qPCR assay. <strong>Conclusions:</strong> The established CRISPR-GBS platform can detect GBS in a rapid, accurate, easy-to-operate, and cost-efficient manner. It offered a promising tool for the intrapartum screening of GBS colonization.
DOI: 10.11578/1608139
title: Therapeutic candidates for SARS-CoV-2: in silico predictions and selected experimental validation for public small molecule libraries and designed synthetic antibodies

description: Results of computationally screening public small-molecule libraries for binding to Covid-19 protein binding sites (four sites on two proteins, initially), including both mechanistic (physics-based) calculations and machine learning-based predictions, along with selected experimental validation. In addition, model predictions of safety and pharmacokinetic parameters are provided for each molecule, as well as molecular descriptors that are features used in the models. Finally, selected designed synthetic antibodies and binding predictions are also available.
DOI: 10.17632/66c3fb2kkr.2
title: Data for: A separable nonlinear least squares approach for double-diode photovoltaic model parameter extraction

description: The data set was obtained through parameter estimation in double-diode photovoltaic model using a separable nonlinear least squares method. First, the "curve fitting case study" gives the measured I-V curve data and the curve fitting result of a RTC France solar cell and a PhotoWatt PWP201 module. Second, the "datasheet fitting case study" gives the measured I-V curve and the estimated current from datasheet information. Third, the "NREL fitting parameters" gives the fitting parameters of a large-scale I-V curve repository available from the National Renewable Energy Laboratory.
DOI: 10.5281/zenodo.1476551
title: Updated Gravity Spy Data Set

description: We present the first version of the training set used in the Gravity Spy citizen science project. This training set, curated further from the one discussed in detail here and available here 10.5281/zenodo.1476156, was utilized to train the convolutional neural network employed in the Gravity Spy project. We anticipate moving forward to release more labelled Gravity Spy data sets, including a data set containing the annotations provided by our citizen science volunteers.

<strong>Data Set Information</strong>

There are three files provided in this data set


	<strong>trainingset_v1d1_metadata.csv</strong>

	
		This file has many columns, <em>gravityspy_id, label, </em>and <em>sample_type.</em><em> gravityspy_id </em>is the unique 10 character hash given to every Gravity Spy sample. <em>label</em> is the string label of the sample. <em>sample_type </em>indicates whether this sample was used in the paper for testing training or validating the models. This is provided for those who would like to do direct comparisons to the network described in the paper.
		Additional columns contain some metadata information about the glitch
		
			<em>event_time,ifo,peak_time,peak_time_ns,start_time,start_time_ns,duration,search,process_id,event_id,peak_frequency,central_freq,bandwidth,channel,amplitude,snr,confidence,chisq,chisq_dof,param_one_name,param_one_value,</em>
		
		
	
	
	<em><strong>trainingsetv1d1.h5</strong></em>
	
		This file contains the exact arrays used in the paper for every Gravity Spy sample. Each Gravity Spy sample is defined by four different images with varying temporal duration, <em>0.5, 1.0, 2.0, and 4.0</em> second, respectively. This also determines the naming conventions of the PNGs: <em>interferometer_gravityspyid_spectrogram_duration.png (e.g. H1_Fv3p6eROvA_spectrogram_0.5.png, H1_Fv3p6eROvA_spectrogram_1.0.png, H1_Fv3p6eROvA_spectrogram_2.0.png, H1_Fv3p6eROvA_spectrogram_4.0.png</em>).
		This file contains all the information needed for each sample in the Gravity Spy dataset (i.e. the label, the sample type of the sample, the unique id of the sample, and the image data for that sample.
		
			/1080Lines/validation/xUEyaWr34c Group<br>
			/1080Lines/validation/xUEyaWr34c/0.5.png Dataset {1, 140, 170}<br>
			/1080Lines/validation/xUEyaWr34c/1.0.png Dataset {1, 140, 170}<br>
			/1080Lines/validation/xUEyaWr34c/2.0.png Dataset {1, 140, 170}<br>
			/1080Lines/validation/xUEyaWr34c/4.0.png Dataset {1, 140, 170}
		
		
	
	
	<strong>trainingsetv1d1.tar.gz</strong>
	
		Contains the raw PNGs of the Gravity Spy training set.
		The structure of the folder is <em>/"label"/"sample_type"/"pngs"</em>
	
	


<strong>Data Set Parsing Information</strong>

To read and crop out the plot axis and labels of the provided PNGs, the following small python code using scikit-image should work.

from skimage import io

image_data = io.imread("filename_of_image")

x=[66, 532]; y=[105, 671]

image_data = image_data[x[0]:x[1], y[0]:y[1], :3]