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System Fingerprint Recognition for Deepfake Audio (SFR) - Compressed Set

<div>The rapid progress of deep speech synthesis models has posed significant threats to society such as malicious manip</div> <div>ulation of content. This has led to an increase in studies aimed at detecting so-called “deepfake audio”. However, existing works</div> <div>focus on the binary detection of real audio and fake audio. In real-world scenarios such as model copyright protection and</div> <div>digital evidence forensics, it is needed to know what tool or model generated the deepfake audio to explain the decision. This</div> <div>motivates us to ask: ‘Can we recognize the system fingerprints of deepfake audio?’ In this paper, we present the first deepfake</div> <div>audio dataset for System Fingerprint Recognition (SFR) and conduct an initial investigation. We collected the dataset from</div> <div>the speech synthesis systems of seven Chinese vendors that use the latest state-of-the-art deep learning technologies, including</div> <div>both clean and compressed sets. In addition, we provide extensive benchmarks and research findings to facilitate the further development of system fingerprint recognition methods. The dataset is publicly available. </div> <div> </div> <div>The subsets 01, 02, and 03 represent the training set, development set, and test set, respectively.</div> <div> </div> <div> <div>This data set is licensed with a CC BY-NC-ND 4.0 license.</div> </div>

System Fingerprint Recognition for Deepfake Audio (SFR) - Clean Set

Data set from the design of experiment assessment rubric, from university students / future teachers

<p>Using an assessment rubric comprising three levels of success, we evaluated six aspects of designing an experiment, which we call dimensions. This way, specific challenges were identified in two of the dimensions: forming a hypothesis and manipulating the experimental variables, where students often failed to succeed and get lower scores.</p> <p>The data presented here were collected from four (4) diagnostic worksheets, which were made available to students at the beginning of the semester, where a theoretical introduction to the course is given, with reference to learning theories and their use in the teaching of Physics. Therefore, data were collected prior to discussions about the inquiry-based approach, experimental investigations, and the scientific content, such as the concepts and phenomena mentioned in the worksheets. </p> <p>For the present study, we slightly modified and used a rubric that has been proposed to assess the DoE by primary and secondary school students (Lefkos et al., 2011). The modified rubric, has already been tested in a pilot study with fruitful results (Lefkos, 2024).</p> <p>Lefkos, I. (2024). An Assessment Rubric for Future Teachers’ Ability to Design Experiments. In C. Fazio & P. Logman (Eds.), <em>Challenges in Physics Education</em> (pp. 105–117). Springer. https://doi.org/10.1007/978-3-031-48667-8_7</p> <p>Lefkos, I., Psillos, D., & Hatzikraniotis, E. (2011). Designing experiments on thermal interactions by secondary-school students in a simulated laboratory environment. <em>Research in Science & Technological Education</em>, <em>29</em>(2), 189–204. https://doi.org/10.1080/02635143.2010.533266</p>

Data Set "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions"

<p>This data set accompanies the publication "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions" by Johannes Vornweg and Christoph R. Jacob (TU Braunschweig, Germany) </p> <p>It contains the following files:</p> <p><br>Directory 1_structures:</p> <p>    PDB files of all structures used for the test calculations. <br>    The PDB files correspond to the protonated structures obtained <br>    as described in the main text.</p> <p><br>Directory 02_figure_scripts:</p> <p>    Jupyter Notebook for generating all plots included in the manuscript, <br>    including raw numerical data.</p> <p><br>Directory 03_input_scripts:</p> <p>    - min_congrad.mdp: input file for partial optimization of protonated <br>         protein--ligand complexes with Gromacs</p> <p>    PyADF input scripts:</p> <p>    - sp_single.pyadf: single-point calculations of separate protein and ligand<br>    - sp_complex.pyadf: single-point calculation of protein-ligand complex<br>    - mfccmbe3.pyadf: MFCC and MFCC-MBE(2) calculations of P-L interaction energy</p> <p>    These scripts can be used with PyADF v1.5 (DOI: 10.5281/zenodo.13236550)</p>

Summary statistics of the gene-based collapsing analyses for COVID-19 severity within the DeCOI WGS data set

<p>These are the summary statistics of the gene-based collapsing analyses for COVID-19 severity within the DeCOI Whole-Genome Sequencing data set from the European subcohort (n=1,017).</p> <p>Case - control definitions:<br>Ex (file: DeCOI_EUR_Ex_RVAS.tsv.gz): 272 cases (required mechanical ventilation or died because of COVID-19 = WHO score 6-10); 362 controls (not hospitalized = WHO score 1-3)<br>B1 (file: DeCOI_EUR_B1_RVAS.tsv.gz): 655 cases (at least hospitalized = WHO score 4-10); 362 controls (not hospitalized = WHO score 1-3)</p> <p>Column description:<br>CHROM: Chromosome of the respective gene<br>ID: Column of the format: [Ensemble Gene ID].[mask which was used to select variants].[upper alle frequency cut-off]<br>ALLELE0: The reference allele<br>ALLELE1: The alternative allele and the effect allele - here all variants which pass allele frequency and mask filters (see ID-column)<br>A1FREQ: Total frequency of Allele 1<br>A1FREQ_CASES: Frequency of Allele 1 in cases<br>A1FREQ_CONTROLS: Frequency of Allele 1 in controls<br>N: Count of individuals that were used in the analysis<br>N_CASES: Count of cases that were used in the analysis<br>N_CONTROLS: Count of controls that were used in the analysis<br>TEST: Test-mode of regenie - here an additive model was used<br>BETA: Estimated effect size given as beta<br>SE: Standard error of BETA<br>LOG10P: Negative decadic logarithm of the p-value</p> <p>Method:<br>Please refer to our accompanying manuscript for a detailed description. We conducted gene-based collapsing analysis using regenie 3.2.4 in logistic regression mode (without step 1). Covariates used were sex, age, age*age, age*sex and the first 10 principal components derived from common variants.</p> <p>Contact for further information:<br>dac_decoi_hostgenetics@listen.uni-bonn.de</p>

D1.4 - Consolidated OA data set for responsible research practices and trust in science: POIESIS National and Regional Database

<p>Deliverable 1.4 of the POIESIS project is a consolidated open access data set for responsible research practices and trust in science. D1.4 consists of two databases: the POIESIS National Database and the POEISIS Regional Database. The method and rationale behind the construction of all indicators in both databases are laid out in Work Package 1’s previous deliverable, D1.3 (Bauer et al., 2024). The two database are accompanied by a Guidance Sheet which provides details and relevant notes regarding the structure of the two databases. </p> <p> </p>

Data Set

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Example data sets to accompany SeismicUnixGui tutorial (>V87.1)

<h1>SeismicUnixGui example data set for use with SeismicUnixGui (>V87.x)</h1> <p>The data set that is included was collected (2018) during a week-long summer orientation internship program hosted (RESESS-Research Experience in Solid Earth Science for Students ) operated by the Earthscope Consortium.  The data set accompanies a tutorial for use of SeismicUnixGui.</p> <p><br>SeismicUnixGui is a graphical user interface (GUI) to select parameters for Seismic Un*x (SU) modules. <br>Seismic Un*x (Stockwell, 1999) is a widely distributed free software package for processing seismic reflection<br>and signal processing.  Perl/Tk is a mature, well-documented and free object-oriented graphical user interface for Perl.  <br>In a classroom environment, shell scripting of SU modules engages students and helps focus on the theoretical limitations<br>and strengths of signal processing.  However, complex interactive processing stages, e.g., selection of optimal <br>stacking velocities, killing bad data traces, or spectral analysis requires advanced flows beyond the scope of <br>introductory classes.  In a research setting, special functionality from other free seismic processing software <br>such as SioSeis (UCSD-NSF) can be incorporated readily via an object-oriented style to programming.<br>An object oriented approach is a first step toward efficient extensible programming of multi-step processes, <br>and a simple GUI simplifies parameter selection and decision making.  Currently, in SeismicUnixGui, Perl 5 packages <br>wrap nearly 300 SU modules that are used in teaching undergraduate and first-year graduate student <br>classes (e.g., filtering, display, velocity analysis and stacking).  Perl packages (classes) can advantageously<br>add new functionality around each module and clarify parameter names for easier usage.  For example, through <br>the use of methods, packages can isolate the user from repetitive control structures, as well as replace the <br>names of abbreviated parameters with self-describing names.  Moose, an extension of the Perl 5 object system, <br>greatly facilitates an object-oriented style.  Perl wrappers are self-documenting via Perl programming document <br>markup language.</p>

"Toy Data Set" referenced in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (https://doi.org/10.1101/2024.08.22.609110)

<p>This data set, referenced as "toy data set" in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (<a href="Lint-to-article">https://doi.org/10.1101/2024.08.22.609110</a>), mimics a high-throughput screening data set. To demonstrate the application of our analysis framework described in the main article this toy data set was generated. It contains in total 1536000 individual transient signals, splitted in 5 batches of each 200 plates in 1536-well plate format. Five distinct signal classes were used to resemble typical shapes encountered in biological experiments. Fequency of occurrences for each class is reported in the main article.</p>

Enhancing Collaborative Care in Schizophrenia: A Comparative Analysis of ICF Core Sets Assessments by Occupational Therapy and Mental Health Social Work Students

<p>This repository contains the R code, dataset, and README file for the network analysis of ICF (International Classification of Functioning, Disability, and Health) assessments. The study compares the assessments conducted by occupational therapy students and mental health social work students, analyzing centrality measures and Bridge Expected Influence within their respective networks.</p>

Data set for "Stabilization of nesquehonite for application in carbon capture utilization and storage"

<p>The dataset contains all relavent raw data from the study titile, "Stabilization of nesquehonite for application in carbon capture utilization and storage". </p>

set of exploration data and parameters for h24/5ad agent based model

<p>Different set of data used for exploration, used for reproductibility, updated with HigherProp parameters</p>

RISMEAU data set

<p><span>The RISMEAU project (</span><em><span>RISques liés aux résidus de Médicaments, biocides et antibiorésistance d’origine humaine et vétérinaire sur les ressources en EAU du bassin versant de l’Arve</span></em><span> – Risks related to residues of pharmaceuticals and biocides, and antimicrobial resistance of human and veterinary origin on the water resources of the Arve catchment area) was </span><span>implanted from 2018 to 2024 on the SIPIBEL observatory. It was devoted to the evaluation of (i) transfers of and processes related to pharmaceutical residues and biocides from both urban sludge and manure spread on fields as fertilisers, and (ii) the environmental impacts of land spreading, <span> </span>in particular the ecotoxicological risks and antimicrobial resistance dissemination. The methodology was based on the physico-chemical, ecotoxicological and antimicrobial resistance (AMR) characterisation of the infiltrated water and soil matrices samples, and focused on organic waste products application at locally representative agronomic rates. This dataset can be reused by other researchers for comparison with their own investigations on this emerging topic under different contexts and conditions, and may contribute to international reviews.</span></p> <p><span>The database includes in total 26217 values, measured on 351 samples of organic waste products (OWP), soil, infiltrated water, earthworms, plants with 3136 usual physico-chemical values, 15469 values on pharmaceuticals and biocides concentrations, 6827 bioassay values (ecotoxicity and phytotoxicity) and 785 values of antimicrobial resistance indicators.</span></p>

Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Oulu facility

<p>This data set covers core network and end-to-end measurements at the Oulu facility, part of the 6G-SANDBOX infrastructure.</p>

Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Malaga facility

<p>This data set covers core network and end-to-end measurements at the Malaga facility, part of the 6G-SANDBOX infrastructure.</p>

Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Berlin facility

<p>This data set covers core network and end-to-end measurements at the Berlin facility, part of the 6G-SANDBOX infrastructure.</p>

Data set from the 6G-SANDBOX platforms benchmarking and assessment for different documented trial networks - Athens facility

<p>This data set covers core network and end-to-end measurements at the Athens facility, part of the 6G-SANDBOX infrastructure.</p>

Data set for "Multi-frequency SuperDARN HF radar observations of the ionospheric response to the October 2023 annular solar eclipse"

<p>This data set contains rawACF and sounding mode data files from the Christmas Valley East (CVE) SuperDARN radar and manually scaled ionogram parameters from the Boulder (BC840) Digisonde used in the paper "Multi-frequency SuperDARN HF radar observations of the ionospheric response to the October 2023 annular solar eclipse" submitted to Geophysical Research Letters.</p>

opencc-by-4.0Jul 2024View details →

data_set for "Time-resolved sensing of electromagnetic fields with single-electron interferometry"

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Validation Set

<p>Training data (validation set) used for training of the neural network</p>