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3,983 results for “Persons”
Non-personalized HRIR databases with and without floor reflections
<p>Non-personalized HRIR databases in SOFA format [1] with and without floor reflections. Floor reflections were simulated with a plywood board between a Head-And-Torso Simulator (HATS) and a dodecahedral loudspeaker. These recordings were captured at the anechoic chamber of the University of Aizu.</p> <p><strong>Apparatus</strong></p> <ul> <li><strong>Head and Torso Simulator (HATS):</strong> 5128-C (Brüel & Kjær—B&K, Denmark).</li> <li><strong>Preamplifier:</strong> NEXUS preamplifier (B&K).</li> <li><strong>Audio interface:</strong> Babyface (RME, Germany).</li> <li><strong>Software used:</strong> ScanIR [2].</li> <li><strong>Loudspeaker:</strong> Self-built regular dodecahedral loudspeaker (7.2 kg). This could be circumscribed by a sphere of 25 cm in diameter. Drivers (P800K—FOSTEX, Japan) were attached to 3 mm acrylic plates.</li> <li><strong>Audio source: </strong>A one-second sine-sweep tone sampled at 96 kHz generated with ScanIR.</li> <li><strong>Floor simulation: </strong>Plywood board 181x91x1.2 cm weighing 13 kg (density ρ = 658 kg/m3 i.e., a relatively firm board).</li> <li><strong>Locations:</strong> 72 azimuths from 0º to 355º in steps of 5º (counterclockwise measured) with a combination of elevation φ = [±60º, ±30º, 0º] at a distance of 153 cm from the center of the HATS’ head to the center of the loudspeaker.</li> </ul> <p>Other details on the procedure and how this was used in our research are found in [3]. HRIRs were capture with and without the plywood board. they are called here “echoic” and “anechoic,” respectively. In addition to the original sampling rate, we include here resampled versions at 44.1 and 48 kHz.</p> <p><strong>Filenames</strong></p> <p>For both anechoic and echoic databases, download:<br> AizuEle@[<em>sample rate</em>].zip</p> <p>Other SOFA files:<br> AizuEle[<em>XXX</em>]@[<em>sample rate</em>].sofa, replace ‘<em>XXX</em>’ with ‘WIF’ for echoic recordings and with ‘WOF’ for anechoic ones.</p>
Personal pronoun systems in the languages of the Greater Burma Zone
<p>A collection of 51 languages of Myanmar and surrounding areas (the <em>"Greater Burma Zone"</em>), listing the systems of personal pronouns with some additional information and metadata about the language, including the source. This dataset was used in Müller & Weymuth (2017) to show the correlation between the societal structure (hierarchical vs. non-hierarchical) and the types of personal pronoun systems (hierarchical vs. grammatical).</p> <p>The *.zip file contains 53 files, most *.docx, and some *.odt, and a template file. It also contains an R script that we used to produce the map shown in the paper. This R file is rather crude and many things were entered manually instead of reading it automatically from the dataset.</p> <p><strong>Source:</strong><br> Müller, André & Rachel Weymuth. 2017. "How Society Shapes Language: Personal Pronouns in the Greater Burma Zone." In: <em>Asiatische Studien – Études Asiatiques </em>71(1), 409–432. DOI: 10.1515/asia-2016-0021 (URL: https://www.degruyter.com/downloadpdf/j/asia.2017.71.issue-1/asia-2016-0021/asia-2016-0021.pdf)</p>
Dataset for "ZnO decorated Graphene-based NFC tag for personal NO2 exposure monitoring during a workday"
<p>Dataset with all measurements performed and related to the publication "ZnO decorated Graphene-based NFC tag for personal NO2 exposure<br>monitoring during a workday" Published in Sensors MDPI 2024 by A. Santos and co-workers.</p>
HALOC Dataset | WiFi CSI-based Long-Range Person Localization Using Directional Antennas
<p><strong>WiFi CSI-based Long-Range Person Localization Using Directional Antennas</strong></p> <p>This repository contains the HAllway LOCalization (HALOC) dataset and WiFi system CAD files as proposed in <a href="https://openreview.net/forum?id=AOJFcEh5Eb" target="_blank" rel="noopener">[1]</a>.</p> <p><strong>PyTroch Dataloader</strong></p> <p>A minimal PyTorch dataloader for the HALOC dataset is provided at: <a href="https://github.com/StrohmayerJ/HALOC" target="_blank" rel="noopener">https://github.com/StrohmayerJ/HALOC</a></p> <p><strong>Dataset Description</strong></p> <p>The HALOC dataset comprises six sequences (in .csv format) of synchronized WiFi Channel State Information (CSI) and 3D position labels. Each row in a given .csv file represents a single WiFi packet captured via ESP-IDF, with CSI and 3D coordinates stored in the "data" and ("x", "y", "z") fields, respectively.</p> <p>The sequences are divided into training, validation, and test subsets as follows:</p> <table> <tbody> <tr> <td><strong>Subset</strong></td> <td><strong>Sequences</strong></td> </tr> <tr> <td>Training</td> <td>0.csv, 1.csv, 2.csv and 3.csv</td> </tr> <tr> <td>Validation</td> <td>4.csv</td> </tr> <tr> <td>Test</td> <td>5.csv</td> </tr> </tbody> </table> <p> </p> <p><strong>WiFi System CAD files</strong></p> <p>We provide CAD files for the 3D printable parts of the proposed WiFi system consisting of the main housing (housing.stl), the lid (lid.stl), and the carrier board (carrier.stl) featuring mounting points for the Nvidia Jetson Orin Nano and the ESP32-S3-DevKitC-1 module. </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].</p> <p>[1] Strohmayer, J., and Kampel, M. (2024). “WiFi CSI-based Long-Range Person Localization Using Directional Antennas”, <em>The Second Tiny Papers Track at ICLR 2024</em>, May 2024, Vienna, Austria. <a href="https://openreview.net/forum?id=AOJFcEh5Eb" target="_blank" rel="noopener">https://openreview.net/forum?id=AOJFcEh5Eb</a></p> <p>BibTeX citation:</p> <pre>@inproceedings{<br>strohmayer2024wifi,<br>title={WiFi {CSI}-based Long-Range Person Localization Using Directional Antennas},<br>author={Julian Strohmayer and Martin Kampel},<br>booktitle={The Second Tiny Papers Track at ICLR 2024},<br>year={2024},<br>url={https://openreview.net/forum?id=AOJFcEh5Eb}<br>}</pre>
3DO Dataset | On the Generalization of WiFi-based Person-centric Sensing in Through-Wall Scenarios
<p><strong>On the Generalization of WiFi-based Person-centric Sensing in Through-Wall Scenarios</strong></p> <p>This repository contains the <strong>3DO dataset</strong> proposed in <a href="https://doi.org/10.1007/978-3-031-78354-8_13">[1]</a>.</p> <p><strong>PyTroch Dataloader</strong></p> <p>A minimal PyTorch dataloader for the 3DO dataset is provided at: <a href="https://github.com/StrohmayerJ/3DO/tree/main">https://github.com/StrohmayerJ/3DO</a></p> <p><strong>Dataset Description</strong></p> <p>The 3DO dataset comprises 42 five-minute recordings (~1.25M WiFi packets) of three human activities performed by a single person, captured in a WiFi through-wall sensing scenario over three consecutive days. Each WiFi packet is annotated with a 3D trajectory label and a class label for the activities: no person/background (0), walking (1), sitting (2), and lying (3). (<strong>Note:</strong> The labels returned in our dataloader example are walking (0), sitting (1), and lying (2), because background sequences are not used.)</p> <p>The directories <code>3DO/d1/</code>, <code>3DO/d2/</code>, and <code>3DO/d3/</code> contain the sequences from days 1, 2, and 3, respectively. Furthermore, each sequence directory (e.g., <code>3DO/d1/w1/</code>) contains a <code>csiposreg.csv</code> file storing the raw WiFi packet time series and a <code>csiposreg_complex.npy</code> cache file, which stores the complex Channel State Information (CSI) of the WiFi packet time series. (If missing, <code>csiposreg_complex.npy</code> is automatically generated by the provided dataloader.)</p> <p>Dataset Structure:</p> <p>/3DO</p> <p>├── d1 <em><-- day 1 subdirectory</em></p> <p> └── w1 <em><-- sequence subdirectory</em></p> <p> └── csiposreg.csv <em><-- raw WiFi packet time series</em></p> <p> └── csiposreg_complex.npy <em><-- CSI time series cache</em></p> <p>├── d2 <-- day 2 subdirectory</p> <p>├── d3 <-- day 3 subdirectory</p> <p> </p> <p>In [1], we use the following training, validation, and test split:</p> <table> <tbody> <tr> <td><strong>Subset</strong></td> <td><strong>Day</strong></td> <td><strong>Sequences </strong></td> </tr> <tr> <td>Train</td> <td>1</td> <td>w1, w2, w3, s1, s2, s3, l1, l2, l3</td> </tr> <tr> <td>Val</td> <td>1</td> <td>w4, s4, l4</td> </tr> <tr> <td>Test</td> <td>1</td> <td>w5 , s5, l5</td> </tr> <tr> <td>Test</td> <td>2</td> <td>w1, w2, w3, w4, w5, s1, s2, s3, s4, s5, l1, l2, l3, l4, l5</td> </tr> <tr> <td>Test</td> <td>3</td> <td>w1, w2, w4, w5, s1, s2, s3, s4, s5, l1, l2, l4</td> </tr> </tbody> </table> <p><em>w = walking, s = sitting and l= lying</em></p> <p><strong>Note: </strong>On each day, we additionally recorded three ten-minute background sequences (b1, b2, b3), which are provided as well.</p> <p> </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper <a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">[1]</a>.</p> <p><a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">[1]</a> Strohmayer, J., Kampel, M. (2025). On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios. In: Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15315. Springer, Cham. <a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-78354-8_13</a></p> <p>BibTeX citation:</p> <pre>@inproceedings{strohmayerOn2025, author="Strohmayer, Julian and Kampel, Martin",<br> title="On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios",<br> booktitle="Pattern Recognition",<br> year="2025",<br> publisher="Springer Nature Switzerland",<br> address="Cham",<br> pages="194--211",<br> isbn="978-3-031-78354-8" }</pre>
Curated Estonian National Bibliography - persons
<p>This curated dataset is derived from the persons authority file of the Estonian National Bibliography (ENB), a comprehensive catalog of publications written in Estonian, published in Estonia, or focusing on Estonian culture and people. Designed for computational analysis, this dataset adapts the original authority file for research and cultural exploration. Through a systematic process of filtering, cleaning, and harmonizing, the ENB dataset is presented in a streamlined tabular format that retains rich metadata while improving accessibility. Fields selected for inclusion are harmonized and, where possible, linked to external sources, offering an optimized and reproducible resource for historical, cultural, and bibliographic research.</p>
Historical Person Register, Tyrol 15th, 16th century
<p>The dataset consists of a file in CSV format (UTF 8). The historical person register contains late medieval / ENHG person names within the Tyrolean mining documents Hs. 37 and Hs. 1587 (15th and 16th century) as well as a modern standardisation of the names. The persons received unique Identifiers. The names are also split into title, first name, last name, occupation and descriptor. Furthermore, the year is added, provenance if available, and alternate writings of the names within the text.</p> <p>The project “Text Mining Medieval Mining Texts” (2019-2022) processed two historical mining sources: “Verleihbuch der Rattenberger Bergrichter” ( Hs. 37, 1460-1463) and “Schwazer Berglehenbuch” (Hs. 1587, approx. 1515) stored by the Tyrolean Regional Archive, Innsbruck (Austria). The central research objective of T.M.M.M.T. is the extraction and representation of the legal relationships between people, claims and mines over space and time. Furthermore it deals with the semantically opening and visualisation of the montanistic network of two tyrolean mining regions.</p> <p>Citeable Transcripts are online available:<br> Hs. 37 DOI: 10.5281/zenodo.6274562<br> Hs. 1587 DOI: 10.5281/zenodo.6274928</p> <p>View also the facsimiles and transcripts on Mining Hub: https://transkribus.eu/r/mining-hub/#/</p> <p>The research project (2019-2022) was carried out at the university of Innsbruck and funded by go!digital next generation programme of the Austrian Academy of Sciences.</p>
Persons_09/29/22
Documentation material from the Mastic pilot of the Mingei project
Krefeld_Important_Persons_Portraits_Mingei
<p>Documentation material from the Silk pilot of the Mingei project</p>
RADAR – Guideline on Personal Data
<p><strong>The HTML publication is available at <a href="https://nfdi4culture.de/go/E5380" target="_blank" rel="noopener">https://nfdi4culture.de/go/E5380</a>.</strong></p> <p>This guideline is intended to help you understand what information falls under the term “personenbezogene Daten” and which of these can be published on RADAR4Culture.</p>
Survey with game development companies on personal data protection
<p><strong>Dataset linked to the article: </strong>Investigating the Implementation of Data Protection Laws in Brazilian Game Companies: An Initial Study</p>
Duhumbi Personal Narratives - Sound files
<p>This data set contains all the original sound files of the personal narratives in the 'Grammar of Duhumbi' (Brill) published in 2019. A separate Zenodo DOI contains all the Toolbox-compatible .txt files and Transcriber .trs files with the transcribed, parsed, glossed, translated examples (DOI 10.5281/zenodo.1406176). The following list contains the sound file names, the shortcut code for the sentence names and the title of the text.</p> <ul> <li>[CHUK230512D1A] / CMT / The story of the former CM’s death</li> <li>[CHUK230512C1A] / LHT / The history of Laphek village</li> <li>[CHUK230512B1] / THT / Hunting takin</li> <li>[CHUK260413A3A]/ ACK / Alcohol consumption</li> <li>[CHUK131014] / DTPK / Chasing the demons</li> </ul> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
User stories and xAPI statements for "A mobile campus application as a sensor node for Personal Learning Environments"
<p>This dataset provides the full user stories and xAPI statements as used in the prototype described in the article "A mobile campus application as a sensor node for Personal Learning Environments". It consists of two PDF documents described below. The files were created as part of the master thesis of Hendrik Geßner.</p> <p>"User Stories.pdf" contains a complete list of user stories with required context information, existing portlets and a category. The process that led to this collection is described very briefly in the article mentioned above, a graphical explanation is available in the attached image "Use case process complete.jpg"</p> <p>"xAPI Statements.pdf" contains all xAPI statements used in the prototype described in the article mentioned above. Dynamic elements such as names or IDs are highlighted on color. The statements appear in the following order: attended, used, loggedin, wasat, opened, closed, joined, left.</p>
Personalized in silico model for radiation-induced pulmonary fibrosis | (source code, simulation input+output data)
<p>This repository concerns the supplementary material data of the research article entitled "<em>Personalised in silico model for radiation-induced pulmonary fibrosis</em>" that is published in the Royal Society Interface journal (rsif.royalsocietypublishing.org). More specifically, the repository contains the source code of the radiation-induced pulmonary fibrosis simulator, the results produced from the medical image analysis of this study (CT scans and RT dosage maps) for each patient case, the input files necessary to run the simulator and the corresponding output produced respectively. Each patient ID corresponds to each case documented in the research article.</p>
Accurately Inferring Personality Traits from the Use of Mobile Technology
<p>This dataset contains the features extracted from Spatio-Temporal Mobility and Context of Use and the Big5 scores from the 50-item IPIP survey of 55 volunteers from 6 countries located in 2 continents.</p> <p>The authors predict the Big5 traits by fitting 5 regularized linear regression models, one per trait, and select the regularization parameter and evaluate the prediction performance through nested leave-one-out cross validation.</p> <p><em><strong>Feature extraction pipeline</strong></em></p> <p>For each volunteer, we start the pipeline with 5 time series encoding, in time, her WGS84 coordinates (latitude and longitude), measurements related to her smartphone's battery (charging status and level), surrounding WiFi APs and BT devices, and whether her phone was connected to a WiFi access point.</p> <p>First, we refine the 5 raw time series to accurately describe the spatio-temporal mobility and the context of our volunteers. For example, we create a binary time series that peaks when the user is at home, or when the user is at work, and so on.</p> <p>Next, we process both the refined and the raw time series to extract the features, as follows:</p> <ol> <li><strong>Statistical Features</strong>: We divide the raw time series in intervals of one day. We aggregate the different values within each day into a single numerical measurement (e.g., by computing the average, the count of unique values, the information entropy, or the repetitiveness). Finally, we aggregate the measurements obtained across all days into a single value --- the value of that feature for the selected user --- by measuring the mean (<em>avg</em>), the standard deviation (<em>std</em>), and the coefficient of variation (<em>cov</em>). Features prefixed with <em>avg</em>, <em>std</em>, or <em>cov, </em>have been extracted as described here.</li> <li><strong>Spectral Analysis Features</strong>: We first apply the DFT to the raw time series. Then, we measure: <ol> <li>The frequency of highest energy (we prefix its name with <em>top_frequency</em>);</li> <li>The <em>periodicity</em> of the series in the frequency domain;</li> <li>The energy at the daily and weekly frequencies (<em>daily_energy </em>and <em>weekly_energy</em>);</li> <li>The frequency, the periodicity, and the daily and weekly energy obtained after processing the time series with Welch's method and a two weeks window (<em>w_top_frequency</em>, <em>w_periodicity</em>, <em>w_daily_energy, w_weekly_energy);</em></li> <li>The euclidean distance between the DFT and a pure sine wave with period equivalent to the top frequency of the series (<em>distance_from_sine</em>).</li> </ol> </li> </ol> <p>The string <em>b_day </em>in each name specifies that the features only consider business days (i.e. they exclude holidays and weekends).</p> <p>The 5 columns named O, C, E, A, and N, score the users on the Big5 and represent the prediction targets.</p> <p><em><strong>Source code</strong></em></p> <p>The Python source code developed to engineer and evaluate the embeddings is available <a href="https://www.dropbox.com/s/0nmivoftdfzq4ss/OCEAN_sources.zip?dl=0">here</a>.</p>
Dataset for: Owner-ascribed personality profiles distinguish domestic cats that capture and bring home wild animal prey
<p>Dataset allowing repetition of the analyses in the above paper, comprising personality scores and predation data, with details of cat characteristics. See readme.txt file.</p>
Witchcraft trials in the Czech lands: Data on persons, places, charges, punishments, and material substances
<p>This is the most comprehensive digital dataset of witchcraft trials in the Czech lands since the earliest case in 1491 until as late as 1785. The dataset covers 257 cases. It records the year, the suspects' names, their sex, places of residence (including geographic coordinates), charges, results of the trial, places of interrogation (including geographic coordinates), and the material substances they were charged of using to perform magic. The dataset allows researchers to conduct systematic and quantitative research into early modern withcraft trials, crime and punishment, as well as the imagery of magic and evil-doing. It also allows to include the Czech lands in broader quantitative studies of European witchcraft trials on a large temporal and geographic scale. Five B.A. theses have been written at Masaryk University, Brno, Czech Republic on the basis of this dataset.</p> <p>The dataset does not cover all known trials. Based on data availability, we did our best to cover what was published and reasonably accessible, but we did not perform original archival research. Our very rough estimate is that up to 100 further specific witchcraft trials in the Czech lands could be identified in published material, and archival work could reveal further ca. 250-800 cases.</p>
Extensive crowdsourced dataset of in-situ evaluated binaural soundscapes of private dwellings containing subjective sound-related and situational ratings along with person factors to study time-varying influences on sound perception — research data
<p><strong>Abstract:</strong></p> <p>The soundscape approach highlights the role of situational factors in sound evaluations; however, only a few studies have applied a multi‐domain approach including sound‐related, person‐related, and time‐varying situational variables. Therefore, we conducted a study based on the Experience Sampling Method to measure the relative contribution of a broad range of potentially relevant acoustic and non‐auditory variables in predicting indoor soundscape evaluations. Here we present the comprehensive dataset for which 105 participants reported temporally (rather) stable trait variables such as noise sensitivity, trait affect, and quality of life. They rated 6.594 situations regarding the soundscape standard dimensions, perceived loudness, and the saliency of its sound components and evaluated situational variables such as state affect, perceived control, activity, and location. To complement these subject‐centered data, we additionally crowdsourced object‐centered data by having participants make binaural measurements of each indoor soundscape at their homes using a low‐(self‐)noise recorder. These recordings were used to compute (psycho‐)acoustical indices such as the energetically averaged loudness level, the A‐weighted energetically averaged equivalent continuous sound pressure level, and the A‐weighted five‐percent exceedance level. This complex hierarchical data can be used to investigate time‐varying non‐auditory influences on sound perception and to develop soundscape indicators based on the binaural recordings to predict soundscape evaluations.</p> <p><strong>Content:</strong></p> <ul> <li><a href="https://zenodo.org/record/7858848/files/01%20StudyDescription.pdf">01 StudyDescription.pdf </a> <ul> <li>Description of the field study.</li> <li>Information about the methods and materials used.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/02%20Dataset.csv">02 Dataset.csv</a> <ul> <li>The dataset, consisting of 93 variables describing 6594 observations taken by 105 participants.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/03%20VariableDescriptions_EnglishPersonQuestionnaire.pdf">03 VariableDescriptions_EnglishPersonQuestionnaire.pdf</a> <ul> <li>Descriptions of all variables, their measurement scale, scale ranges and levels.</li> <li>Questions and task descriptions of the Experience Sampling Method questionnaire in German language with an English translation.</li> <li>English translations of questions asked in the person questionnaire.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/04%20ESM-Questionnaire.pdf">04 ESM-Questionnaire.pdf</a> <ul> <li>Screenshots of the original Experience Sampling Method questionnaire with English translations.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/05%20PersonQuestionnaire_OriginalGermanVersion.pdf">05 PersonQuestionnaire_OriginalGermanVersion.pdf</a> <ul> <li>Original version of the person questionnaire in German language.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/06%20HelpTexts.pdf">06 HelpTexts.pdf</a> <ul> <li>Descriptions of the study task.</li> <li>Explanations of the scales used in the questionnaire.</li> <li>Explanations of the sound categories and the soundscape composition.</li> <li>Explanation of the operation of the recording device.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_README.md">AcousticFeatures_README.md</a> <a href="https://zenodo.org/api/files/3d784540-c0f4-412f-8742-df1db6f5401d/TimeSeries_and_Spectrograms_README.md?versionId=9291496c-d2c6-4151-96f1-a2ad99e1a540"> </a> <ul> <li>Descriptions of the structure of the AcousticFeatures_xxx.csv and .zip files.</li> <li>Analyis settings used in Artemis Suite to generate the acoustic features.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_SingleValues.csv">AcousticFeatures_SingleValues.csv</a> <ul> <li>All acoustic features, aggregated to single values per feature, recording, and channel.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectra.csv">AcousticFeatures_Spectra.csv</a> <ul> <li>Time-averaged 1/3 octave spectra of each channel of each recording, A-weichted and un-weighted.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectrograms.zip">AcousticFeatures_Spectrograms.zip</a> <ul> <li>13188 .csv files with un-weighted spetrograms of each channel of each recording.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_TimeSeries.zip">AcousticFeatures_TimeSeries.zip</a> <ul> <li>A .csv file containing LAeq and LZeq time series of each channel of each recording.</li> </ul> </li> </ul> <p><strong>Publications refering to this dataset:</strong></p> <p>Versümer, Siegbert; Steffens, Jochen; Weinzierl, Stefan (currently under review): "The role of loudness predictions, personal and situational factors in day-to-day loudness assessments of indoor soundscapes."</p> <p><strong>Funding:</strong></p> <p>This study was sponsored by the German Federal Ministry of Education and Research. “FHprofUnt” funding code: 13FH729IX6. </p> <p><strong>License: </strong></p> <p>CC 4.0 BY, <a href="https://creativecommons.org/licenses/by/4.0/legalcode">https://creativecommons.org/licenses/by/4.0/legalcode</a></p> <p><strong>Version history:</strong></p> <p>Details can be found in the <a href="https://zenodo.org/api/files/a15d6a91-1a35-4b5e-a7ec-da8a9bcbee2b/Changelog.md">Changelog.md</a> file.</p> <ul> <li> V.01.0. March 7, 2023: Initial publication. <a href="https://doi.org/10.5281/zenodo.7193938">https://doi.org/10.5281/zenodo.7193938</a></li> <li> V.01.1. April 25, 2023. <a href="https://doi.org/10.5281/zenodo.7858848">https://doi.org/10.5281/zenodo.7858848</a></li> </ul>
Elevated temperature effects on animal personality: hormonal stress response underlying behavioural differences in the American bullfrog
<p>Dataset for research paper submitted to Animal Behaviour</p> <p>Behavioural_data.csv: raw data for how individual bullfrogs performed in six different trials on an 8-arm maze before and after they were submitted to thermal stress. Behaviours analyzed: movements against the wall of the maze, posture changes, total ambulatory distance (m), and time on the centre of the arena (s).</p> <p>Hormone_data.csv: raw hormone (corticosterone and testosterone) data collected from individual bullfrogs in four different time points: baseline, 12 hours after stress, 24 days after stress, and 47 days after stress.</p> <p>Mass_data.csv: raw mass data collected from individual bullfrogs at the beginning and end of the experiment. SVL = snout-vent length. Body index is calculated as the residuals of a linear regression between mass as dependent variable and SVL as independent variable.</p>
Replication Package: Exploring the Relationship Between Personality Traits and User Feedback
<p>This is the replication package for the paper titled 'Automated User Feedback Analysis: Processing the Feedback Quantity and Quality' accepted for the AffectRE23 workshop track at RE 2023.</p> <p>Full abstract:</p> <p>Previous research has studied the impact of developer personality in different software engineering scenarios, such as team dynamics and programming education. However, little is known about how user personality affect software engineering, particularly user-developer collaboration. Along this line, we present a preliminary study about the effect of personality traits on user feedback. 56 university students provided feedback on different software features of an e-learning tool used in the course. They also filled out a questionnaire for the Five Factor Model (FFM) personality test. We observed some isolated effects of neuroticism on user feedback: most notably a significant correlation between neuroticism and feedback elaborateness; and between neuroticism and the rating of certain features. The results suggest that sensitivity to frustration and lower stress tolerance may negatively impact the feedback of users. This and possibly other personality characteristics should be considered when leveraging feedback analytics for software requirements engineering.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.