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Data set: Delivery of Carbon Dioxide to an Electrode Surface Using a Nanopipette

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SNID template data set v2.0.0 extended to 2 microns (1024 and 2048 wavelength bins)

This set of SNID templates extends the v2.0.0 set out to 2 microns (wavelength range 2500 Ang-2 microns). It should be used when the input spectrum has data extending beyond 1 micron, since the original templates-2.0 set only covers 2500-10000 Ang. Two versions of the data set are published, one with 1024 wavelength bins, the other with 2048 wavelength bins.  Reading template files...sn1979C sn1980K sn1981B sn1983N sn1983V sn1984A sn1984L sn1986G sn1987A sn1988L sn1989B sn1990B sn1990I sn1990K sn1990N sn1990O sn1990U sn1990aa sn1991A sn1991M sn1991N sn1991T sn1991ar sn1991bg sn1992A sn1992H sn1992ar sn1993J sn1993ac sn1994D sn1994I sn1994M sn1994Q sn1994S sn1994T sn1994ae sn1995D sn1995E sn1995F sn1995ac sn1995ak sn1995al sn1995bd sn1996C sn1996L sn1996X sn1996Z sn1996ab sn1996ai sn1996bk sn1996bl sn1996bo sn1996bv sn1996cb sn1997E sn1997Y sn1997bp sn1997bq sn1997br sn1997cn sn1997cy sn1997dc sn1997dd sn1997do sn1997dq sn1997dt sn1997ef sn1997ei sn1998S sn1998T sn1998V sn1998ab sn1998aq sn1998bp sn1998bu sn1998bw sn1998co sn1998de sn1998dh sn1998dk sn1998dm sn1998dt sn1998dx sn1998ec sn1998ef sn1998eg sn1998es sn1999X sn1999aa sn1999ac sn1999aw sn1999bh sn1999by sn1999cc sn1999cl sn1999cp sn1999cw sn1999da sn1999di sn1999dn sn1999dq sn1999ee sn1999ef sn1999ej sn1999ek sn1999em sn1999ex sn1999gd sn1999gh sn1999gi sn1999gp sn2000B sn2000E sn2000H sn2000bh sn2000bk sn2000ce sn2000cf sn2000cn sn2000cp sn2000cu sn2000cw sn2000cx sn2000dg sn2000dk sn2000dm sn2000dn sn2000fa sn2001E sn2001G sn2001N sn2001V sn2001ah sn2001ay sn2001az sn2001bf sn2001bg sn2001br sn2001cj sn2001ck sn2001cp sn2001da sn2001eh sn2001el sn2001en sn2001ep sn2001ex sn2001fe sn2001fh sn2001gc sn2002G sn2002ap sn2002aw sn2002bf sn2002bo sn2002cd sn2002cf sn2002ck sn2002cr sn2002cs sn2002cu sn2002cx sn2002de sn2002dj sn2002dl sn2002do sn2002dp sn2002ef sn2002er sn2002es sn2002eu sn2002fb sn2002fk sn2002ha sn2002hd sn2002he sn2002hu sn2002hw sn2002ic sn2002jg sn2002jy sn2002kf sn2003U sn2003W sn2003Y sn2003bg sn2003cg sn2003ch sn2003cq sn2003du sn2003fa sn2003gn sn2003hu sn2003hv sn2003ic sn2003it sn2003iv sn2003kc sn2003kf sn2004L sn2004S sn2004as sn2004at sn2004aw sn2004bd sn2004bg sn2004bk sn2004dj sn2004dt sn2004ef sn2004eo sn2004et sn2004fu sn2004fz sn2004gc sn2004gs sn2005A sn2005M sn2005am sn2005bc sn2005be sn2005bf sn2005bl sn2005bo sn2005cc sn2005cf sn2005cg sn2005cs sn2005el sn2005eq sn2005eu sn2005gj sn2005hc sn2005hf sn2005hj sn2005hk sn2005iq sn2005kc sn2005ke sn2005ki sn2005kl sn2005ls sn2005lu sn2005mc sn2005mz sn2005na sn2006D sn2006H sn2006N sn2006S sn2006X sn2006ac sn2006aj sn2006ak sn2006al sn2006ax sn2006az sn2006bp sn2006bq sn2006br sn2006bt sn2006bw sn2006bz sn2006cc sn2006cf sn2006cj sn2006cm sn2006cp sn2006cq sn2006cz sn2006em sn2006eq sn2006et sn2006eu sn2006ev sn2006gj sn2006gr sn2006gt sn2006gz sn2006hb sn2006kf sn2006le sn2006lf sn2006mo sn2006nz sn2006oa sn2006ot sn2006sr sn2006te sn2007A sn2007F sn2007S sn2007Y sn2007ae sn2007af sn2007al sn2007ap sn2007au sn2007ax sn2007ba sn2007bc sn2007bd sn2007bj sn2007bm sn2007bz sn2007ca sn2007cg sn2007ci sn2007co sn2007cq sn2007fb sn2007fs sn2007hj sn2007if sn2007jg sn2007kk sn2007le sn2007nq sn2007qe sn2007sr sn2007ux sn2008A sn2008C sn2008D sn2008L sn2008Q sn2008R sn2008Z sn2008ae sn2008af sn2008ar sn2008bf snls03D3bb  done  Loaded         4388  spectra out of          333  templates

A set of seamless 0.05-degree, daily SIF product data (FGSIF)

A set of seamless 0.05-degree, daily SIF product data (FGSIF)

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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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.

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

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.

opengpl-3.0-or-laterJul 2024View details →

STILT footprints data set 3

This repository contains the third batch of training data sets of measurement footprints. The footprints are used to train the deep learning model presented in our paper titled "FootNet v1.0: Development of a machine learning emulator of atmospheric transport". Preprint of the manuscript could be accessed at https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1526/. The footprints are provided in Numpy compressed array format, which could be decompressed with Python 3.10.6 and NumPy 1.23.4.

opencc-by-4.0May 2024View details →

Data set for paper: "Numerical analysis of plastic deformation evolution in polycrystalline copper during cyclic loading with different frequencies"

The dataset contains information necessary for performing the numerical analysis presented in the related paper. The input data are suited for the finite element code Z-set (http://www.zset-software.com/). The resulting data from the numerical calculations and source data for paper figures can be used for further analysis. These data are provided in ASCII format in text files and can be processed by any relevant software.

Electronic structure properties of the SmartNanoTox data set (nanomaterials) for the use of meta models assesing cytotoxicity

Important set of electronic structure properties data on the SmartNanoTox dataset consisting of large molecular systems representing coated materials. The data were used to study lung inflammation within a service offered through EU Horizon 2020 NanoCommons project.