cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/tp6k8wky8m.1
title: FluTAS: A GPU-accelerated finite difference code for multiphase flows

description: We present the Fluid Transport Accelerated Solver, FluTAS, a scalable GPU code for multiphase flows with thermal effects. The code solves the incompressible Navier-Stokes equation for two-fluid systems, with a direct FFT-based Poisson solver for the pressure equation. The interface between the two fluids is represented with the Volume of Fluid (VoF) method, which is mass conserving and well suited for complex flows thanks to its capacity of handling topological changes. The energy equation is explicitly solved and coupled with the momentum equation through the Boussinesq approximation. The code is conceived in a modular fashion so that different numerical methods can be used independently, the existing routines can be modified, and new ones can be included in a straightforward and sustainable manner. FluTAS is written in modern Fortran and parallelized using hybrid MPI/OpenMP in the CPU-only version and accelerated with OpenACC directives in the GPU implementation. We present different benchmarks to validate the code, and two large-scale simulations of fundamental interest in turbulent multiphase flows: isothermal emulsions in HIT and two-layer Rayleigh-Bénard convection. FluTAS is distributed through a MIT license and arises from a collaborative effort of several scientists, aiming to become a flexible tool to study complex multiphase flows.
DOI: 10.17632/b9dxsybhm9.1
title: Multi-Laboratory Hematoxylin and Eosin Staining Variance Unsupervised Machine Learning Dataset

description: We provide the generated dataset used for unsupervised machine learning in [1]. The data is in CSV format and contains all principal components and ground truth labels, per tissue type. Tissue type codes used are; C1 for kidney, C2 for skin, C3 for colon, and 'PC' for the principal component. Please see the original design in [1] for feature extraction specifications. Features have been extracted independently for each tissue type.

Reference:
Prezja, F.; Pölönen, I.; Äyrämö, S.; Ruusuvuori, P.; Kuopio, T. H&E Multi-Laboratory Staining Variance Exploration with Machine Learning. Appl. Sci. 2022, 12, 7511. https://doi.org/10.3390/app12157511
DOI: 10.17632/vg3hb74rvb.1
title: Parents' perceptions about children's online security: Qualitative interviews

description: We conducted semi-structured interviews with 25 parents. This dataset contains the transcripts of the interviews.
Our study’s goal was to clearly understand parents’ views on and perceptions of children’s cybersecurity. Considering that the term “cybersecurity” may be confusing or not clear to some parents, we used the term “online security” in our
questions, as both terms represent similar concerns and meanings in our context

Questions we asked:
1.	Considering the current situation, all the children are using the internet and digital platforms more than before for their educational activities and entertainment. How concerned are you about their online security? 
2.	Do you know about a real story in your environment concerning the safe use of the internet and digital tools for children? Can you say something about it?
3.	What security concerns do you have for your children when they are online? 
4.	How much do your children know about online security?
5.	In your opinion, what kind of security awareness do children need when they use the internet?
6.	When it comes to educating the children about online security, in your opinion, who should be responsible? Parents or the schools?  
7.	What do you do at home to ensure online security for your children?
8.	Can you tell me an episode when your child learned about online security?
9.	What do you think is most challenging in ensuring online security for your children?
DOI: 10.17632/dpnjgd89mp.1
title: Data for "Welfare Effects of Health Insurance Reform: The Role of Elastic Medical Demand"

description: This file contains data and codes for figures and tables in the paper titled "Welfare Effects of Health Insurance Reform: The Role of Elastic Medical Demand".
DOI: 10.17632/x4nb352t5j.1
title: Data for: The Legacy of the Home Owners' Loan Corporation and the Political Ecology of Urban Trees and Air Pollution in the United States

description: Percentage change in tree canopy coverage (2001 & 2011) in HOLC areas in the US.
DOI: 10.17632/5pmnkshffm.2
title: Network traffic and code for machine learning classification

description: The dataset is a set of network traffic traces in pcap/csv format captured from a single user. The traffic is classified in 5 different activities (Video, Bulk, Idle, Web, and Interactive) and the label is shown in the filename. There is also a file (mapping.csv) with the mapping of the host's IP address, the csv/pcap filename and the activity label. Activities: Interactive: applications that perform real-time interactions in order to provide a suitable user experience, such as editing a file in google docs and remote CLI's sessions by SSH. Bulk data transfer: applications that perform a transfer of large data volume files over the network. Some examples are SCP/FTP applications and direct downloads of large files from web servers like Mediafire, Dropbox or the university repository among others. Web browsing: contains all the generated traffic while searching and consuming different web pages. Examples of those pages are several blogs and new sites and the moodle of the university. Vídeo playback: contains traffic from applications that consume video in streaming or pseudo-streaming. The most known server used are Twitch and Youtube but the university online classroom has also been used. Idle behaviour: is composed by the background traffic generated by the user computer when the user is idle. This traffic has been captured with every application closed and with some opened pages like google docs, YouTube and several web pages, but always without user interaction. The capture is performed in a network probe, attached to the router that forwards the user network traffic, using a SPAN port. The traffic is stored in pcap format with all the packet payload. In the csv file, every non TCP/UDP packet is filtered out, as well as every packet with no payload. The fields in the csv files are the following (one line per packet): Timestamp, protocol, payload size, IP address source and destination, UDP/TCP port source and destination. The fields are also included as a header in every csv file. The amount of data is stated as follows: Bulk : 19 traces, 3599 s of total duration, 8704 MBytes of pcap files Video : 23 traces, 4496 s, 1405 MBytes Web : 23 traces, 4203 s, 148 MBytes Interactive : 42 traces, 8934 s, 30.5 MBytes Idle : 52 traces, 6341 s, 0.69 MBytes The code of our machine learning approach is also included. There is a README.txt file with the documentation of how to use the code.
DOI: 10.17632/3rvzb5fj5w.3
title: Pseudo Dataset and Code for Adelino, Schoar and Severino (2024) "Credit Supply and House Prices: Evidence from Mortgage Market Segmentation"

description: This paper develops a difference-in-differences estimator that uses annual changes in the conforming loan limit and the 80% loan-to-value (LTV) threshold to isolate the impact of easier access to credit on house prices. Houses that become eligible for financing with an 80% LTV conforming loan increase in value by about $1.17 per square foot, controlling for a rich set of characteristics. Our estimates imply a local elasticity of house prices to interest rates below 6, which suggests that interest rates are capitalized into prices to a lesser extent than proposed by studies relying on more aggregate variation.
DOI: 10.17632/pd93m3c4rz.1
title: PV DC Series Arc Simulation Data

description: Solar photovoltaic (PV) systems have emerged as a pivotal player in the pursuit of clean energy solutions. As the adoption of solar technology continues to increase, it becomes imperative to address the some of the challenges associated with these systems. One such challenge that demands meticulous attention is the occurrence of DC series arcs.

DC series arcs, characterized by unintentional electrical discharges across series-connected components, represent a potential threat to the integrity and efficiency of solar energy systems. Identifying and mitigating these arc faults is crucial not only for enhancing the overall performance of solar panels but also for safeguarding against the risk of electrical fires.

This dataset was generated using a Simulink model based on the most common topology for PV DC power converters and known properties of arc events. The model consists of 4 boost converters connect to 4 individual PV panels and each can be individually controlled. Each converter is operated with a PWM module commanded by a duty cycle value and operating at a fixed frequency of 64kHz. The currents of each PV string are measured and added into a single signal, simulating existing HW architecture for arc detection in residential PV systems. The arc event is simulated by injecting gaussian noise limited to a specific frequency band with a range of 10 to 50 kHz.

This dataset is intended to help in the evaluation of arc detection algorithms based on current measurements.

Each file in the dataset contains 6 columns as follows: simulation timestamp, 250kHz sample rate current data, slow DC current measurement for each string in the system (4 strings)

The name of the file described the conditions in which that set was obtained and follows this pattern:
test_ + 
A + current target on string A +
B + current target on string B +
C + current target on string C +
D + current target on string D +
A + voltage target on string A +
B + voltage target on string B +
C + voltage target on string C +
D + voltage target on string D +
A + impedance on string A +
B + impedance on string B +
C + impedance on string C +
D + impedance on string D +
+ _trans + A, B, C or D +
+ _arc + 0 or 1 + .csv
DOI: 10.17632/9dcs5nvwtw.1
title: Test Anxiety, Wellbeing, Generalised Anxiety, Panic

description: Data were collected from 918 participants aged 13-19 years from 8 secondary schools/ sixth form colleges (academic track upper secondary education), located in England and Wales, in a cross-sectional design. Data were collected electronically and participants were prompted if they had missed a question hence there were no missing data. Test anxiety was measured using the 16-item Multidimensional Test Anxiety Scale (Putwain et al., 2020). School-Related Wellbeing measured using the six-item School-Related Wellbeing Scale (Loderer et al., 2016). Generalized Anxiety and Panic were measured using the sub-scales from the Revised Children’s Anxiety and Depression Scale (Chorpita et al., 2005). In addition socio-demographic data was collected for gender (male, female, or prefer not to say), age, year group (The English/ Welsh equivalent of Grade), ethnic heritage (Asian, Black, White, Other, or mixed heritage - any combination of the other categories) and eligibility for free school meals (a proxy for low income; yes or no). Data were saved as an SPSS .sav file (that includes all items for the above scales, response ranges and anchors, and the categorical coding for the aforementioned socio-demographic data. Research questions were as follows: 1. Does the MTAS demonstrate measurement invariance for gender, age and free school meals? 2. How does the test anxiety relate to school-related wellbeing, and symptoms of generalized anxiety and panic? We hypothesised that text anxiety would be negatively related to school-related wellbeing and positively related to generalized anxiety and panic. 3. Can a cut score for the MTAS be reliably established to predict clinical significance for generalized anxiety and panic? 4. How closely are the items for text anxiety, school-related wellbeing, generalized anxiety, and panic related? References Chorpita, B. F., Moffitt, C., & Gray, J. (2005). Psychometric properties of the Revised Child Anxiety and Depression Scale in a clinical sample. Behaviour Research and Therapy, 43, 309-322. https://doi.org/10.1016/j.brat.2004.02.004 Loderer, K., Vogl, E., & Pekrun, R. (2016, August 24-27). Students’ well-being at school revisited: Development and initial validation of a unidimensional self-report scale [Conference session]. International Conference on Motivation (ICM), Thessaloniki, Greece. http://www.psy.auth.gr/el/conference/programme. Putwain, D.W., von der Embse, N.P., Rainbird, E.C., & West, G. (20209). The development and validation of a new Multidimensional Test Anxiety Scale (MTAS). European Journal of Psychological Assessment. Advance online pulication, https://doi.org/10.1027/1015-5759/a000604
DOI: 10.17632/978nzm2hbn.1
title: ANSYS-Fluent Results for Fluid Flow and Heat Transfer in a Cross-Flow Plate Heat Exchanger

description: This is the results in .plt format for fluid flow and heat transfer in a Cross-Flow Plate Heat Exchanger in the following conditions: fluid in both sides: air at 1 atm, T0 (hot side) = 298 K, T0 (cold fluid) = 273 K, Channels dimensions: L = W = 300 mm, Hf = Hr = 10 mm, plate material: aluminium, thickness Hp= 1 mm, Re in both sides 6000.
DOI: 10.17632/c7fjbxcgj9.2
title: Endoscopy Artefact Detection (EAD) Dataset

description: We aim to establish a first large and comprehensive dataset for "Endoscopy artefact detection". The provided data is obtained from 6 different data centres that includes John Radcliffe Hospital, Oxford, UK; ICL Cancer Institute, Nancy, France; Ambroise Paré Hospital of Boulogne-Billancourt, Paris, France; Istituto Oncologico Veneto, Padova, Italy; University Hospital Vaudois, Lausanne, Switzerland; Botkin Clinical City Hospital, Moscow. This dataset is unique and represent multi-tissue (gastroscopy, cystoscopy, gastro-oesophageal, colonoscopy), multi-modal (white light, fluorescence, and narrow band imaging), inter patient and multi-population (UK, France, Russia, and Switzerland) endoscopic video frames. Videos were collected from patients on a first-come-first-served basis at Oxford, while randomized sampling was done at French centres and only cancer patients were chosen at the Moscow centre. Videos at these centres were acquired with standard imaging protocols using endoscopes built by different companies like Olympus, Biospec, and Karl Storz. While building our dataset, we have randomly mixed these data with no exclusion criteria. All images has been anonymised carefully and thereby no patient information is expected to be visible in this data. The released data is a part of Endoscopy Artefact Detection (EAD2019) IEEE ISBI'19 challenge. With the Endoscopy Artefact Detection Challenge (EAD2019), we aim to identify hindrances like saturations, motion blur, specular reflections, bubbles, imaging artefacts, contrast and instrument using revolutionary techniques in artificial intelligence. This step is vital in obtaining more sensible temporal information that can motivate and help both endoscopists and image analysts to develop more comprehensive computer-assisted tools. The challenge is sub-divided into three tasks: 1) Multi-class artefact detection: Localization of bounding boxes and class labels for 7 artefact classes for given frames 2) Region segmentation: Precise boundary delineation of detected artefacts 3) Detection generalization: Detection performance independent of specific data type and source Details of this challenge can be found at: https://ead2019.grand-challenge.org Useful tools for this dataset: https://sharibox.github.io/EAD2019/
DOI: 10.17632/jy6rvth8w2.1
title: Amazon user item temporal bipartite rating network

description: This bipartite network contains product ratings from the Amazon online shopping website. The rating scale ranges from 1 to 5, where 5 denotes the most positive rating. Nodes represent users and products, and edges represent individual ratings. More information about the network is provided here: http://konect.uni-koblenz.de/networks/amazon-ratings Files: meta.amazon-ratings -- Metadata about the network out.amazon-ratings -- The adjacency matrix of the network in space separated values format, with one edge per line The meaning of the columns in out.amazon-ratings are: First column: ID of from node Second column: ID of to node Third column: edge weight Fourth column: timestamp of the edge Complete documentation about the file format can be found in the KONECT handbook, in the section File Formats, available at: http://konect.uni-koblenz.de/publications All files are licensed under a Creative Commons Attribution-ShareAlike 2.0 Germany License. For more information concerning license visit http://konect.uni-koblenz.de/license. Use the following References for citation: @MISC{konect:2016:amazon-ratings, title = {Amazon ratings network dataset -- {KONECT}}, month = oct, year = {2016}, url = {http://konect.uni-koblenz.de/networks/amazon-ratings} } @inproceedings{konect:lim2010, author = {Lim, Ee-Peng and Nguyen, Viet-An and Jindal, Nitin and Liu, Bing and Lauw, Hady Wirawan}, title = {Detecting Product Review Spammers Using Rating Behaviors}, booktitle = {Proc. Int. Conf. on Information and Knowledge Management}, year = {2010}, pages = {939--948}, } @inproceedings{konect:jindal2008, author = {Jindal, Nitin and Liu, Bing}, title = {Opinion Spam and Analysis}, booktitle = {Proc. Int. Conf. on Web Search and Web Data Mining}, year = {2008}, pages = {219--230}, } @inproceedings{konect:mukherjee2012, author = {Mukherjee, Arjun and Liu, Bing and Glance, Natalie}, title = {Spotting Fake Reviewer Groups in Consumer Reviews}, booktitle = {Proc. Int. Conf. on World Wide Web}, year = {2012}, pages = {191--200}, } @inproceedings{konect, title = {{KONECT} -- {The} {Koblenz} {Network} {Collection}}, author = {Jérôme Kunegis}, year = {2013}, booktitle = {Proc. Int. Conf. on World Wide Web Companion}, pages = {1343--1350}, url = {http://userpages.uni-koblenz.de/~kunegis/paper/kunegis-koblenz-network-collection.pdf}, url_presentation = {http://userpages.uni-koblenz.de/~kunegis/paper/kunegis-koblenz-network-collection.presentation.pdf}, }
DOI: 10.17632/y6dvssx73b.1
title: Chest Diseases Using Different Medical Imaging and Cough Sounds

description: This dataset contains cough sound images (CSI), chest X-rays (CXR), and CT scan images of several chest diseases such as COVID-19, lung cancer (LC), consolidation lung (COL), atelectasis (ATE), tuberculosis (TB), pneumothorax (PNEUTH), edema (EDE), pneumonia (PNEU) and normal (NOR). The dataset was collected from Al-Shafa Hospital, Multan, Pakistan in collaboration with Apple Pharma medicine dealer company. The statistics of the dataset are presented in Table 1. The meta-information of the patient is not provided. However, the dataset is about 20% of female and 80% of male CSI, CXR, and CT scan images. In addition, CXR, CSI, and CT scans of individuals aged 41 to 62 years were collected. Table 1. Statistics of the chest diseases dataset. Sr# Chest Diseases CSI CXR CT 1 COVID-19 28 5 17 2 Lungs Cancer 30 7 5 3 Consolidation Lung 18 5 3 4 Atelectasis 25 4 4 5 Tuberculosis 27 4 6 6 Pneumothorax 18 5 4 7 Edema 18 5 9 8 Pneumonia 16 10 15 9 Normal 24 6 8
DOI: 10.17632/7kjgwzhdtk.1
title: Behavioural Science and Nudge Interventions Database for SDG Acceleration 2024 (Naik et al.)

description: A database of 201 behavioural and nudge interventions with empirically proven results novelly linked to the UN Sustainable Development Goals. Interventions were implemented between 1982 to 2020 and impacted over 360 million individuals in 43 countries. Each intervention is graded on an impact scale of 1-5 to quantify its impact potential for improving target outcomes. All interventions achieved at least a 1% change in the measured variables, with the maximum change reaching a 1467% increase. The interventions show an impressive average median percentage change of 44.76% per SDG.

This is the first database of its kind to specifically link behavioural and nudge interventions to the UN Sustainable Development Goals. Policymakers can filter for the indicators in which they are underperforming and be presented with targeted, impactful interventions. In a world where resources are limited and time is of the essence, this approach will streamline the decision-making process and enhance the precision and effectiveness of behavioural policy actions.

This database can also be accessed here: https://docs.google.com/spreadsheets/d/1tWy0X2Aq08kIUNYG-Cw5FQsvakdKSyKGKen7_hc2F48/edit?gid=1627241714#gid=1627241714
DOI: 10.17632/jkx9nm58vm.4
title: Contolled-coverage discrete S-transform (CC-DST)

description: The S-transform (ST) is a popular linear time-frequency (TF) transform with hybrid characteristics from the short-time Fourier transform (STFT) and the wavelet transform. It enables a multi-resolution TF analysis and returns globally referenced local phase information, but its expensive computational requirements often overshadow its other desirable features. In this paper, we develop a fully discrete ST (DST) with a controllable TF sampling scheme based on a filter-bank interpretation. The presented DST splits the analyzed signal into subband channels whose bandwidths increase progressively in a fully controllable manner, providing a frequency resolution that can be varied and made as high as required, which is a desirable property for processing oscillatory signals lacked by previously presented DSTs. Thanks to its flexible sampling scheme, the behavior of the developed transform in the TF domain can be adjusted easily; with specific parameter settings, for example, it samples the TF domain dyadically, while by choosing different settings, it may act as a STFT. The spectral partitioning is performed through asymmetric raised-cosine windows whose collective amplitude is unitary over the signal spectrum to ensure that the transform is easily and exactly invertible. The proposed DST retains all the appealing properties of the original ST, representing a local image of the Fourier transform; it requires low computational complexity and returns a modest number of TF coefficients. To confirm its effectiveness, the developed transform is utilized for different applications using real-world and synthetic signals.
DOI: 10.17632/k5pbz9795f.1
title: MultiBanFakeDetect: An Extensive Benchmark Dataset for Multimodal Under-Resource Bangla Fake News Detection

description: The MultiBanFakeDetect dataset consists of a total of 9,600 text-image instances from online forums, news websites, and social media. Covering political, social, Technology, and entertainment themes, the dataset offers balanced real and fake instances. The data distribution within the labels includes 7,680 instances for training, 960 instances for testing, and 960 instances for validation.

Statistical Overview of Text-Image Pair Data Across Different Types of Fake News
=========================================
Types Training Testing Validation
=========================================
Misinformation 1288 161 162
Rumor 1215 152 151
Clickbait 1337 167 167
Non-fake 3840 480 480 
=========================================
Total 7680 960 960

Distribution of Text-Image Pair Data within Labels
=====================================
Label Training Testing Validation
=====================================
1 (Fake) 3840 480 480
0 (Non-Fake) 3840 480 480
=====================================
Total 7680 960 960


Statistical Overview of Text-Image Pair Data Across Different Categories of Fake News
=========================================
Category Training Testing Validation
=========================================
Entertainment 640 80 80
Sports 640 80 80
Technology 640 80 80
National 640 80 80
Lifestyle 640 80 80
Politics 640 80 80
Education 640 80 80
International 640 80 80
Crime 640 80 80
Finance 640 80 80
Business 640 80 80
Miscellaneous 640 80 80
=========================================
Total 7680 960 960