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Nominal FAST5/FASTQ Evaluation Data Set

FAST5/FASTQ data used for accuracy characterization of decoding techniques applied to the HEDGEs DNA-information storage code. FASTQ data is used to evaluate the hard-decoding algorithm as explained by Press et al. in (<a href="https://doi.org/10.1073/pnas.2004821117">https://doi.org/10.1073/pnas.2004821117</a>). FAST5 data is used in evaluation for both our novel Alignment Matrix soft decoder (<a href="https://doi.org/10.5281/zenodo.11454877">https://doi.org/10.5281/zenodo.11454877</a>), and the soft decoder developed by Chandak et al. in the publication (<a href="https://doi-org.prox.lib.ncsu.edu/10.1109/ICASSP40776.2020.9053441">10.1109/ICASSP40776.2020.9053441</a>). Our code repository at <a href="https://doi.org/10.5281/zenodo.11454877">https://doi.org/10.5281/zenodo.11454877</a> includes a GPU accelerated adaptation of Chandak et al.’s algorithm in order to scale analysis on the submitted FAST5 data, and this is the version of code used to evaluate the algorithm’s accuracy and runtime overhead.   Within the archive there are several sub-archives. Explanations for each sub-archive can be found for the corresponding archive name within the README.md file.

Fault-Tolerant Computing with Single Qudit Encoding in a Molecular Spin. Open data set

<div> Data supporting the original figures 2, 3 and 4 (ESI) of the related manuscript. </div>

A data set from a survey investigating the smart approach to selecting good cyber security metrics

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The dataset for the Icarus paper "Crustal origin for olivine in the lunar Shioli crater ejecta boulders: Insights from the geological setting of Theophilus crater and Nectaris basin" [dataset]

The dataset is for the Icarus paper “Crustal origin for olivine in the lunar Shioli crater ejecta boulders: Insights from the geological setting of Theophilus crater and Nectaris basin” [dataset]

openJun 2024View details →

Data set for "Nucleation Mechanisms of Electrodeposited Magnesium on Metal Substrates"

This is the experimental raw data set associated with the following publication: M. Löw, F. Maroni, S. Zaubitzer, S. Dongmo, M. Marinaro, Nucleation Mechanisms of Electrodeposited Magnesium on Metal Substrates, Batteries & Supercaps 2024, e202400250. DOI: <a href="https://doi.org/10.1002/batt.202400250">10.1002/batt.202400250</a>

Data set for paper 'Impact of post-ion implantation annealing on Se-hyperdoped Ge'

Description of the files Files are named by the same figure order appearing in the paper and supplementary material. File types contain: - *.csv: Comma separated values.  - *.fibps: Raw data obtained with the 3D optical profilometer, which can be opened and analyzed by ProfilmOnline at https://www.profilmonline.com/. Figures shown in the paper were croped from the top-left corner with areas of about 60x60 µm2 from the raw data. Alternatively all 3D profilometer data shown in the paper can be obtained and analyzed in https://www.profilmonline.com/shared-folder?token=S32NP82HtLC3 - *.tif: Image generated from scanning electron microscope.    Original paper: https://doi.org/10.1063/5.0213637 Supplementary material: https://doi.org/10.60893/figshare.apl.c.7322108   When using the dataset, please cite the original paper: Xiaolong Liu, Patrick McKearney, Sören Schäfer, Behrad Radfar, Yonder Berencén, Ulrich Kentsch, Ville Vähänissi, Shengqiang Zhou, Stefan Kontermann, Hele Savin; Impact of post-ion implantation annealing on Se-hyperdoped Ge. Appl. Phys. Lett. 22 July 2024; 125 (4): 042102. https://doi.org/10.1063/5.0213637.

opencc-by-nc-nd-4.0Jun 2024View details →

The train and dev sets of ADD 2022

Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (<a href="http://addchallenge.cn/add2022">http://addchallenge.cn/add2022</a>) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks. The ADD 2022 dataset is publicly available. This data set is licensed with a CC BY-NC-ND 4.0 license. If you use this dataset, please cite the following paper: <blockquote> Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li: ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220 </blockquote>

Requirement Set with Defects

Demonstration requirement set with defects to show case ROOT's health checks.

Code and data sets analysed in: "Marine heatwave bleaching causes mass mortality and drives a microbial community reorganisation in an ecologically important temperate sponge" Bell et al. (2024). Global Change Biology

The attached zipped folder contains in-situ, satellite, reanalysis and laboratory measurements, together with R and MATLAB scripts, to reproduce the results in Bell et al. (2024). Marine heatwave bleaching causes mass mortality and drives a microbial community reorganisation in an ecologically important temperate sponge. Global Change Biology. Each folder contains a read me file that describes the enclosed data sets and scripts.

Dephasing-tolerant quantum sensing of transverse magnetic fields with spin qudits. Open data set

Data supporting Figs. 1, 2, 3, 4 of the related manuscript.

Rogue Wave Data Set

The data sets were compiled for our article [1].  The qualitiy controled 30-min windows for the three individual roge wave definitions (1a-c) are included in the 'RAW.zip'. The data sets prepared for training neural networks are included in the file 'Prepared.zip'. The scripts included in 'Prepare_data.ipynb' were utilized to compile the prepared data sets.  Consider listen to <a href="https://www.youtube.com/watch?v=oowTL6Gjuz8">my research talk</a> on this topic.  [1] Breunung, T., & Balachandran, B. (2024). Prediction of freak waves from buoy measurements. Scientific Reports, 14(1), 16048. <a href="https://doi.org/10.1038/s41598-024-66315-3" rel="nofollow">https://doi.org/10.1038/s41598-024-66315-3</a> Data courtesy of <a href="https://cdip.ucsd.edu/" target="_blank" rel="noopener">CDIP</a>. Data set DOI: <a href="https://doi.org/10.18437/C7WC72" target="_blank" rel="noopener">https://doi.org/10.18437/C7WC72</a>.

Abundance Trend Indicator - Models, Prediction, Stacked Environmental Data and Training Set Similarity

# Readme These trained models can be used to predict the abundance trends of New Zealand's forest species and can be used together with the code in https://github.com/lnilya/abundance-trend-indicator Since the process of using the models requires coding expertise and some setting up, please make sure to reach out to ilya.shabanov@vuw.ac.nz for any questions. All files will require the code in the repository to be read and used.  If you want to explore the results generated with these models, please visit https://ati-nz-predictions-7e6f3d514735.herokuapp.com/ for a user-friendly, interactive UI. ## Contents _models: Contains the trained models (Artificial Neural Network (ANN), Random Forest (RF), SVMW (Support vector machine) and GLM (logistic regression)) at different degrees of noise filtering, different datasets and variable sets. The model files also contain test and training scores. To load the files please refer to the readme in the code repository: ttps://github.com/lnilya/abundance-trend-indicator _predictions/_environment: Contains the predictor variables for the study area (New Zealand, 1950-2019) that are needed by the models to make predictions.  _predictions/_similarity: Contains the masks of areas that can be predicted by models and are similar to the training set. _predictions/_ati: Contain the predicted results for the abundance trend. These can be explored on https://ati-nz-predictions-7e6f3d514735.herokuapp.com/

ARC³N: A Collaborative Uncertainty Catalog to Address the Awareness Problem of Model-Based Confidentiality Analysis - Data Set

Data set of the Paper "ARC³N: A Collaborative Uncertainty Catalog to Address the Awareness Problem of Model-Based Confidentiality Analysis". For more information, please see the README.md. For even more information please visit https://abunai.dev

opencc-by-nc-nd-4.0Mar 2024View details →

SVDD Challenge 2024: A Singing Voice Deepfake Detection Challenge (CtrSVDD Track, Test Set)

For more information about SVDD Challenge 2024, please refer to https://challenge.singfake.org/. We have released the test set here. The training and development set is at https://zenodo.org/records/10467648. The Interspeech paper that describes the dataset details and baseline analysis is https://arxiv.org/abs/2406.02438.

Complementary and Alternative Medicine Use and its Impact on Quality of Life among Cancer Patients in Freetown, Sierra Leone: Considerations for a Resource-Limited Setting

Cancer patients are increasingly resorting to complementary and alternative medicine (CAM) to alleviate the effects of the disease and enhance their quality of life (QoL). However, to our knowledge, no study has investigated CAM use and its impact on QoL in patients with cancer in Sierra Leone. Therefore, this study aimed to assess the prevalence of and associated factors with CAM use, and compare the quality of life between CAM users and non-users. This cross-sectional descriptive study was conducted at three health facilities in Freetown viz Connaught Hospital Ola During Children's Hospital, and the Water and Stone Clinic. Data were collected through face-to-face interviews, using an adapted questionnaire. The QoL was assessed using the European Organization for Research and Treatment of Cancer tool. Descriptive analysis was employed, and binary logistic regression tests were used to explore the associated factors of CAM use, with statistical significance set at p<0.05. In total, 204 participants completed the study. The prevalence of CAM use was 48.5%. Among the various CAMs examined in this study, herbal medicines (48.8%) followed by special foods (19.4%) were the most commonly used forms of CAM. The primary reason for CAM use was the management of cancer complications (43.4%). Over half of the CAM users did not disclose their CAM use to their doctors (53.5%). There was a statistically significant association between CAM use and tertiary education (AOR=2.68, 95%CI=1.31-5.49, p=0.007) and chemotherapy treatment (AOR=1.91, 95%CI= 1.06-3.46, p=0.032). There was no considerable impact of CAM on QoL, although financial difficulty was statistically significant among CAM users compared with non-CAM users. This study revealed that CAM use is prevalent among cancer patients, with herbal medicines being the most commonly used form, followed by special foods and prayers.  Patients primarily obtain information about CAM from family, friends, and relatives, and most do not discuss CAM use with their doctors. Tertiary education and chemotherapy were found to be predictors of CAM use. Regarding the impact of CAM on QoL, only financial difficulty was statistically significant.

DATA SET: Perceived instructor's emotional support as a predictor of college students' academic resilience in a Philippine university landscape: From the perspectives of Resilience and Self-Determination Theory

Data from: Perceived instructor’s emotional support as a predictor of college students’ academic resilience in a Philippine university landscape: From the perspectives of Resilience and Self-Determination Theory by Lobo et al. (2024).

One Hand project - Transfer of prosthesis control skill after training in VR, data set pre-test post-test

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I/O Behind the Scenes [Data Set]

<div>This file contains the data set from the paper: "I/O Behind the Scenes: Bandwidth Requirements of HPC Applications With Asynchronous I/O," which was accepted at the Cluster 2024. <div>The Instructions are provided in the <a href="https://github.com/tuda-parallel/TMIO/tree/main/artifacts/cluster24">TMIO GitHub</a>: <a href="https://github.com/tuda-parallel/TMIO/tree/main/artifacts/cluster24">https://github.com/tuda-parallel/TMIO/tree/main/artifacts/cluster24</a></div> </div> <div> </div> <div>After extracting data.zip, the folder named data has the following structure:</div> <div> <pre>data └─── application_traces     ├── HACC-IO     │   ├── 1536     │   ├── 9216     │   └── time_distribution     └── WACOM++         ├── 9216_nolimit         ├── 9614_limit         ├── 96_limit         ├── 96_nolimit         └── time_distribution</pre>   </div>

Data set supplementing "A Use-Case Specific Framework for Designing Representative Vignettes (RepVig) and Evaluating Triage Accuracy of Laypeople and Symptom-Assessment Applications"

This is the de-identified data set used to conduct the analyses of our study "A Use-Case Specific Framework for Designing Representative Vignettes (RepVig) and Evaluating Triage Accuracy of Laypeople and Symptom-Assessment Applications" (<a href="https://doi.org/10.1101/2024.04.02.24305193">https://doi.org/10.1101/2024.04.02.24305193</a>). The data comprises the answers to cases given by laypeople, symptom-assessment applications, and large language models and the corresponding solutions for each case. The cases were developed in the study with a focus on external validity. The dataset contains three datafiles: collected data for laypeople, for symptom-assessment applications, and for large language models.