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Datasets and Supporting Materials for the IPIN 2019 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2019 Competition (Pisa, Italy).</p><p><strong>Contents:</strong></p><ol><li>IPIN2019_Call4Competition: Call for competition and main rules</li><li>IPIN2019_Track03_TechnicalAnnex: Technical annex describing the Track 3 of the competition.</li><li>01-Logfiles: This folder contains a subfolder with the 50 (40 + 10) training logfiles, a subfolder with the 9 validation logfiles, and a subfolder with the 1 blind evaluation logfile as provided to competitors.</li><li>02-Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps, the vector maps and the visualization of the training routes.</li><li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li></ol><p><strong>Please, cite the following works when using the datasets included in this package:</strong></p><ul><li>Jiménez, A. R.; Perez-Navarro, A.; Crivello, A.; Mendoza-Silva, G.; Ortiz, M.; Perul, J.; Seco, F. and Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2019 Competition Track 3 (Smartphone-based, off-site), Zenodo 2019. <a href="http://dx.doi.org/10.5281/zenodo.3606765">http://dx.doi.org/10.5281/zenodo.3606765</a></li><li>Potorti, F.; Park, S.; Palumbo, F.; Girolami, M.; Barsocchi, P.; Lee, S.; Torres-Sospedra, J.; Jimenez Ruiz, A. R.; Perez-Navarro, A.; Mendoza-Silva, G. M.; Seco, F.; Ortiz, M.; Perul, J.; Renaudin, V.; Kang, H.; Park, S. Y.; Lee, J. H.; Park, C. G.; Ha, J.; Han, J.; Park, C.; KIM, K.; Lee, Y.; GYE, S.; Lee, K.; Kim, E.; Choi, J.-S.; Choi, Y.-S.; Talwar, S.; Cho, S. Y.; Ben-Moshe, B.; Sansano, E.; Chidlovskii, B.; Kronenwett, N.; Prophet, S.; Landay, Y.; Marbel, R.; Peng, A.; Wu, B.; MA, C.; Poslad, S.; Selviah, D.; Wu, W.; Ma, Z.; Zhang, W.; Wei, D.; Yuan, H.; Jiang, J.-B.; Liu, J.-W.; Su, K.-W.; Leu, J.-S.; Nishiguchi, K.; Bousselham, W.; Uchiyama, H.; Thomas, D.; Shimada, A.; Taniguchi, R.-I.; Cortés, V.; Lungenstrass, T.; Ashraf, I.; Lee, C.; Usman Ali, M.; Im, Y.; Kim, G.; Eom, J.; Hur, S.; Park, Y.; Opiela, M.; Moreira, A.; Nicolau, M. J.; Pendão, C.; Silva, I.; Meneses, F.; Costa, A.; Trogh, J.; Plets, D.; Chien, Y.-R.; Chang, T.-Y.; Fang, S.-H.; Tsao, Y. The IPIN 2019 Indoor Localisation Competition - Description and Results IEEE Access Vol. 8, pp. 206674-206718, 2020. https://doi.org/10.1109/ACCESS.2020.3037221</li></ul><p><strong>Additional information can be found at:</strong></p><ul><li><a href="http://evaal.aaloa.org/2019/call-for-competitions">http://evaal.aaloa.org/2019/call-for-competitions</a></li></ul><p><strong>For any further questions about the database and this competition track, please contact: </strong></p><ul><li>Joaquín Torres (<a href="mailto:jtorres@uji.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">jtorres@uji.es</a>,<a href="mailto:torres@ubikgs.com?subject=Contact%20from%20Zenodo%203606765">torres@ubikgs.com</a>) UBIK Geospatial Solutions S.L., Spain<br>Institute of New Imaging Technologies, Universitat Jaume I, Spain.</li><li>Antonio R. Jiménez (<a href="mailto:antonio.jimenez@csic.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">antonio.jimenez@csic.es</a>) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain.</li></ul>
Risky decision and happiness task: The Great Brain Experiment smartphone app
<p>This resource consists of data from a risky decision and happiness task that was part of The Great Brain Experiment (GBE) smartphone app. Data were collected from 47,067 participants aged 18+ between March 8, 2013 and October 5, 2015. These anonymous unpaid participants completed the task a total of 91,058 times making approximately 2.7 million choices and 1.1 million happiness ratings in total. This resource represents at least 6,000 hours of task data. A subset of 1,858 participants also completed a depression questionnaire and answered five questions about their depression history.</p>
Gaussian Splatting on the Move - Smartphone Dataset
<p>Casual smartphone 3D scans using three devices: iPhone 15 Pro, Samsung Galaxy S20 FE and Google Pixel 5. The raw data (prefixed "spectacular-rec-") has been captured with the Spectacular Rec applications for Android and iOS, and contains time-synchronized video and IMU data. The <strong>extras</strong> file also contains AprilGrid calibration sequences, as well as pre-computed calibration results, for the Android devices.</p> <p>The data is captured in non-ideal lighting conditions and has a moderate amount of motion blur and rolling shutter artefacts. The included metadata also contains the exposure times and the (Android) rolling shutter readout times, as well as the built-in calibration data, as reported by the devices.</p> <p>The dataset also contains three different processed variants (prefixed with "colmap-"), which are directly trainable with Nerfstudio. In the processed variants, suitable minimally blurry video frames have been selected as key frames and their poses have been registered with COLMAP. In addition, the local linear and angular velocities of each key frame has been estimated using VIO with Spectacular AI Mapping Tools. The "calib-intrinsics" and "orig-intrinsics" variants include manually calibrated and built-in intrinsics, respectively. They depend on the third variant with symbolic links. The third variant has COLMAP-estimated intrinsics, which are relatively inaccurate for the Android data with high levels rolling shutter deformation.</p>
Bluetooth Low Energy based Dataset for Smartphone localization in moving vehicles for electronic toll collection.
<p><span>The collected Dataset presents a data collection based on Bluetooth Low Energy (BLE) technology and the use of smartphones for the purposes of vehicle localization and tracking. </span><span>The system is based on a two-dimensional 8 switched-beam directive panel antennas mounted on an electronic toll collection (ETC) gateway. The multibeam antenna topology is installed on the ETC at a height of 4.7 m and </span><span>the data collected from several experiments, together with the set-up and methodology, are reported here.</span><span> The data collection can be divided into two main sections. First, the system was calibrated in an 8 x 8 meter area below the ETC. The distance between adjacent test points is 0.5 m, with a total of 100 samples measured for each test point in every 8 switched-beam panel antenna. Secondly, the results of dynamic test with vehicles passing at different speeds carrying smartphones and using BLE are detailed. As a validation of the dataset, a benchmark analysis for data visualization and localization/tracking estimation applying MUltiple SIgnal Classification and fingerprinting algorithms is included.</span></p>
Emergent smartphone users' dataset
<p>The effective utilization of a communication channel like calling a person involves two steps. The first step is storing the contact information of another user, and the second step is finding contact information to initiate a voice or text communication. However, the current smartphone interfaces for contact management are mainly textual; which leaves many emergent users at a severe disadvantage in using this most basic functionality to the fullest. Previous studies indicated that less-educated users adopt various coping strategies to store and identify contacts. However, all of these studies investigated the contact management issues of these users from a qualitative angle. Although qualitative or subjective investigations are very useful, they generally need to be augmented by a quantitative investigation for a comprehensive problem understanding. This work presents an exploratory study to identify the usability issues and coping strategies in contact management by emergent users; by using a mixture of qualitative and quantitative approaches. We identified coping strategies of the Pakistani population and the effectiveness of these strategies through a semi-structured qualitative study of 15 participants and a usability study of 9 participants, respectively. We then obtained logged data of 30 emergent and 30 traditional users, including contact-books and dual-channel (call and text messages) logs to infer a more detailed understanding; and to analyse the differences in the composition of contact-books of both user groups. The analysis of the log data confirmed problems that affect the emergent users' communication behaviour due to the various difficulties they face in storing and searching contacts. Our findings revealed serious usability issues in current communication interfaces over smartphones. The emergent users were found to have smaller contact-books and preferred voice communication due to reading/writing difficulties. They also reported taking help from others for contact saving and text reading. The alternative contact management strategies adopted by our participants include: memorizing whole number or last few digits to recall important contacts; adding special character sequence with contact numbers for better recall; writing a contact from scratch rather than searching it in the phone-book; voice search; and use of recent call logs to redial a contact. The identified coping strategies of emergent users could aid the developers and designers to come up with solutions according to emergent users' mental models and needs.</p>
Diffuse-optical data set measured with a smartphone-based sensor on Potato Hill, Oregon, USA
<p>This data set contains both raw data and derived data obtained on Potato Hill, Oregon, on December 17th 2021 using a diffuse-optical, smartphone-based sensor. The raw image files have been converted to an uncompressed Adobe-.dng file format, file names indicate whether the file contains data for the blue (405nm) or red (650nm) laser or spatial calibration data using a 9mm x 9mm calibration pattern. Spectral albedo measurements are contained in the subfilder ./Albedo, the raw images in ./Phone. The root directory contains the matlab code (Matlab R2021b) needed for analysis as well as the derived data.</p> <p>For analyzing the raw data set, use "CameraMatchPotatoHillFinal.m". It wraps around the function "CameraAnalysisFinal.m", which performs the image analysis and least-square fit to resorted and rescaled data, employing in turn the model function "theosurfGInf.m". It saves a derived data set (attenuation, absorption and scattering coefficients, albedos, absorption enhancement factor and snow density.</p> <p>The script "Albedo.m" analyzes the derived data set along with measured albedo and simulated albedo deposited in the file "snicar_120ppb.txt". The obtained albedo curves and black carbon mixing ratio are as shown in the below manuscript.</p> <p>If you wish to use this data set please contact Markus Allgaier at markusa@uoregon.edu with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset. When using the data set within a publication, please cite:</p> <p>Markus Allgaier & Brian Smith, "A Smartphone-Based Sensor for Measuring the Optical Properties of Snow", in preparation, (2022)</p>
Smartphone Disengagement - Autoethnography fieldnotes, classification & interpretation
<p>This dataset contains transcribed fieldnotes from a “methodological assemblage” and technological prototype connecting autoethnography to the practices of self-research in personal science. As an experimental process of personal data gathering, the author used a low-tech device for the active registration of events and their perception, in a case study on disengaging from his smartphone.</p> <p>The dataset contains 267 data points (timestamp and intensity/duration of perceived events) with alternative classification and subclassification categories through thematic analysis, as well as 112 "on spot" fieldnotes and 112 related retrospective interpretation notes (with corresponding references where indicated) from a focused autoethnography intervention, tracking the authors' experience of living without constant access to a smartphone for a month (during May 2021).</p> <p>For more info on the process and the main results of this personal experiment, here's the derived publication: Senabre Hidalgo, E., & Greshake Tzovaras, B. (2023). “One button in my pocket instead of the smartphone”: A methodological assemblage connecting self-research and autoethnography in a digital disengagement study. <em>Methodological Innovations</em>. <a href="https://doi.org/10.1177/20597991231161093">https://doi.org/10.1177/20597991231161093</a></p> <p>As described in the paper, this dataset contains a classification of events based on two complementary approaches:</p> <p><strong>Initial categories (May 2021): </strong>Positive - Negative - Reflection</p> <p><strong>Retrospective categories and subcategories (November 2021):</strong></p> <ol> <li>Response to habit: <ul> <li>Basic impulse: looking for smartphone in pocket / “If I had it now…”</li> <li>Abstinence reaction: attempts to reconnect to “the cloud”</li> <li>Fear of missing out (FOMO) after long periods of time</li> <li>Disruption in basic routine: perception of altered habits</li> </ul> </li> <li>Social context: <ul> <li>Feelings of embarrassment: in relation to others due to feature phone</li> <li>Observing others: people using smartphones around me</li> <li>Meta level: how to share process afterwards (in community, academically)</li> </ul> </li> <li>User experience (UX) practicalities: <ul> <li>Moving around solo: orientation when commuting without GPS</li> <li>Parasitic use of other smartphones (in case of urgent need) </li> <li>Capturing things on the spot (pictures, information, feelings)</li> <li>Local stored culture (music and reading away from screens)</li> <li>Communication problems: dysfunctions in everyday coordination</li> <li>Reconnection need: whenever ending up using the smartphone again</li> </ul> </li> <li>Self-reinforcing / awareness: <ul> <li>Succeeding in intended detox: satisfaction for achievement </li> <li>Full attention to moment / stimulus: mindfulness and concentration</li> <li>Relevant changes in routine: positive reinforcement by new habit</li> </ul> </li> </ol>
Smartphone_data
<p class="MsoNormalCxSpFirst"><strong><span>Objectives:</span></strong><span> Smartphone use has become ubiquitous worldwide.</span><span> Despite smartphone-related convenience, smartphone use has raised concerns regarding addiction among university undergraduates. </span><span>This study aimed to examine the effect of smartphone location, s</span><span>u</span><span>ch as desk, bag, and another room, on working memory, based on electroencephalography parameters, in pharmacy students.</span></p> <p class="MsoNormalCxSpMiddle"><strong><span>Key findings: </span></strong><span>Thirty-six students were enrolled in the study. Smartphone location had no effect on electroencephalography outcomes and working memory. Partial correlation coefficients between alpha and beta and between theta and alpha values were statistically significant when the smartphone was on the desk (r = 0.869, p < 0.0001; r = 0.887, p < 0.0001; respectively), however, those between alpha and beta values were not statistically significant when the smartphone was in the bag and outside the room.</span></p> <p class="MsoNormalCxSpMiddle"><strong><span>Conclusions: </span></strong><span>Smartphone locations did not affect either electroencephalography or working memory findings. Smartphones located in the bag and outside the room seemed to influence students' concentration on the task, but this effect did not affect working memory.</span></p>
The mere presence of a smartphone reduces basal attentional performance
<p>This dataset contains the relevant data for the study “The mere presence of a smartphone reduces basal attentional performance”. </p> <p>The following variables are presented:</p> <p>Person: Numbering of the test subjects</p> <p>Gender: 1- male; 2- female</p> <p>Age: Age of the test subjects</p> <p>Question1 - Question10: The checked values by the test subjects of the questionnaire d-KV-SSS (German Short Version of the Smartphone Questionnaire)</p> <p>WithWithout: The conduction of the test in smartphone presence (with- 1) and out of smartphone presence (without- 2) </p> <p> </p> <p><strong>Values of the attention test (d2-R):</strong></p> <p>APValue: The attention performance value; a person's ability to focus their attention</p> <p>PTOValue: The “Processed target objects” value that indicates the speed of the test processing </p> <p>E% value: The Error % value, the accuracy of a test person in processing the d2-R</p> <p> </p> <p>ValidInvalid: Valid or invalid test performance</p> <p>SmartphoneDependenceSum: The summed-up values of the questionnaire (Question 1 to Question 10), thus the results of the questionnaire d-KV-SSS that indicate a possible tendency towards a smartphone dependency</p> <p>In our study, it was hypothesized that there is a lower attentional performance under smartphone presence. To test this hypothesis, three one-tailed ANOVAs were conducted with the presence and absence of the smartphone as the independent variable and one of the three given values of the d2-R (attention performance, speed value and error value) as the dependent variable. </p> <p>The total score of the d-KV-SSS (“SmartphoneDependenceSum”) indicates a tendency towards smartphone dependence. A one-factor ANOVA was used to examine the subjects' attention performance in connection with a tendency towards smartphone addiction. The presence or absence of the smartphone was the independent variable, and the attention performance score was the dependent variable. </p>
Data set for "EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study"
<p>Dataset has been created for "EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study" paper.</p> <p>We have created a smartphone based behaviour monitoring and recommendation system to aid patients recruited in a weight loss intervention programme.<br>The main interaction element for the patients is <strong>EghiFit</strong> application which was used in context of this dataset for data acquisition and secure transmission to our servers.</p> <p>The data consists of application usage per patient, steps achieved, nutritional information of meals logged, heart rate data, interaction data and more.<br>For more information about the dataset, please take a look at <strong>readme.md</strong> file and our paper.</p>
POLIPHONE: A Dataset for Smartphone Model Identification from Audio Recordings
<p>When dealing with multimedia data, source attribution is a key challenge from a forensic perspective. This task aims to determine how a given content was captured, providing valuable insights for various applications, including legal proceedings and integrity investigations. The source attribution problem has been addressed in different domains, from identifying the camera model used to capture specific photographs to detecting the synthetic speech generator or microphone model used to create or record given audio tracks.<br>Recent advancements in this area rely heavily on machine learning and data-driven techniques, which often outperform traditional signal processing-based methods.<br> However, a drawback of these systems is their need for large volumes of training data, which must reflect the latest technological trends to produce accurate and reliable predictions.<br>This presents a significant challenge, as the rapid pace of technological progress makes it difficult to maintain datasets that are up-to-date with real-world conditions.<br>For instance, in the task of smartphone model identification from audio recordings, the available datasets are often outdated or acquired inconsistently, making it difficult to develop solutions that are valid beyond a research environment.<br>In this paper we present <strong>POLIPHONE</strong>, a dataset for smartphone model identification from audio recordings. It includes data from 20 recent smartphones recorded in a controlled environment to ensure reproducibility and scalability for future research.<br>The released tracks contain audio data from various domains (i.e., speech, music, environmental sounds), making the corpus versatile and applicable to a wide range of use cases.<br>We also present numerous experiments to benchmark the proposed dataset using a state-of-the-art classifier for smartphone model identification from audio recordings.</p>
DAGHAR: A Benchmark for Domain Adaptation and Generalization in Smartphone-Based Human Activity Recognition
<p>DAGHAR benchmark is a curated dataset collection designed for domain adaptation and domain generalization studies in HAR tasks, using inertial sensors such as accelerometers and gyroscopes, from "A benchmark for domain adaptation and generalization in smartphone-based human activity recognition" work. It features raw inertial sensor data sourced exclusively from smartphones. Six public datasets were selected and standardized in terms of accelerometer units of measurement, sampling rate, gravity component, activity labels, user partitioning, and time window size. This standardization process allows for creating a comprehensive benchmark for evaluating the generalization capabilities of HAR models in cross-dataset scenarios.</p> <p>The benchmark is based on the following datasets:</p> <ul> <li><strong>Ku-HAR</strong>, from "Sikder, N. and Nahid, A.A., 2021. KU-HAR: An open dataset for heterogeneous human activity recognition. Pattern Recognition Letters, 146, pp.46-54", avaliable at <a href="https://data.mendeley.com/datasets/45f952y38r/5">Mendeley</a>. Distributed under CC BY 4.0.</li> <li><strong>MotionSense</strong>, from "Malekzadeh, M., Clegg, R.G., Cavallaro, A. and Haddadi, H., 2019, April. Mobile sensor data anonymization. In Proceedings of the international conference on internet of things design and implementation (pp. 49-58)", available at <a href="https://www.kaggle.com/datasets/malekzadeh/motionsense-dataset" target="_blank" rel="noopener">Kaggle</a>. Distributed under Open Data Commons Open Database License (ODbL) v1.0.</li> <li><strong>RealWorld</strong>, from "Sztyler, T. and Stuckenschmidt, H., 2016, March. On-body localization of wearable devices: An investigation of position-aware activity recognition. In 2016 IEEE international conference on pervasive computing and communications (PerCom) (pp. 1-9). IEEE", available at <a href="https://www.uni-mannheim.de/dws/research/projects/activity-recognition/dataset/dataset-realworld/" target="_blank" rel="noopener">this link</a>. We obtained explicitly permission to distribute a copy of the preprocessed data from the original authors.</li> <li><strong>UCI-HAR</strong>, from "Reyes-Ortiz, J.L., Oneto, L., Samà, A., Parra, X. and Anguita, D., 2016. Transition-aware human activity recognition using smartphones. Neurocomputing, 171, pp.754-767", available at <a href="https://archive.ics.uci.edu/dataset/240/human+activity+recognition+using+smartphones">UCI Repository</a>. Distributed under CC BY 4.0.</li> <li><strong>WISDM</strong>, from "Weiss, G.M., Yoneda, K. and Hayajneh, T., 2019. Smartphone and smartwatch-based biometrics using activities of daily living. Ieee Access, 7, pp.133190-133202", available at <a href="https://archive.ics.uci.edu/dataset/507/wisdm+smartphone+and+smartwatch+activity+and+biometrics+dataset">UCI repository</a>. Distributed under CC BY 4.0.</li> </ul>
Dataset Questionnaire Driving Repeat Purchases and E-WOM: How Price, Reputation, Hedonic Appeal, and Social Interaction Shape Consumer Behavior in Indonesia's E-Commerce Smartphone Market
<p>The following dataset is a dataset from a study that investigated price advantage, reputation, hedonic effort, and social interaction influence customer satisfaction, which in turn impacts repurchase intention and e-WOM (electronic word-of-mouth).</p>
Entity Matching on Dataset Smartphone
<p>Entity Matching on Dataset Smartphone</p>
Energy Consumption Estimation of API-usage in Smartphone Apps via Static Analysis
<p>OPEN CALL FOR COLLECTING ENERGY PROFILES @ <a href="https://github.com/AbdulAli/replication-kit-msr-2023">https://github.com/AbdulAli/replication-kit-msr-2023</a></p> <p>Cite this work as:</p> <p>@inproceedings{bangash2023msr,<br> title={Energy Consumption Estimation of API-usage in Mobile Apps via Static Analysis},<br> author={Bangash, Abdul Ali and Jamal, Qasim and Eng, Kalvin and Ali, Karim and Hindle, Abram},<br> booktitle={2023 20th International Conference on Mining Software Repositories (MSR)},<br> pages={5721--5730},<br> year={2023},<br> organization={IEEE}<br> }</p> <p>This is the replication-kit of the paper published at MSR 2023.</p> <p>It includes:</p> <ul> <li>SQLite operations' benchmarks</li> <li>SQLite benchmarks' energy profiles</li> <li>The E-Factor Calculation program</li> </ul>
Source Data for Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips
<p>This data accompanies the study "Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips" published in (Nature) Communications Engineering. This paper focuses on using large and inexpsensive datasets for obtaining information on the dynamics of bridges. In this study, data is collected by smartphones in moving vehicles as the cross over a bridge, in three distinct applications. Smartphone data was collected in controlled field experiments and uncontrolled Uber rides on a long-span suspension bridge in the USA (The Golden Gate Bridge) and an analytical method was developed to accurately recover modal properties. The method was also successfully applied to partially-controlled crowdsourced data collected on a short-span highway bridge in Italy. The results suggest that larve and inexpensive datasets collected by smartphones could play a role in monitoring the health of existing transportation infrastructure.</p> <p>The data provided includes the source data for the figures in the publication as well as the "controlled data" referenced in the study.</p>
Smartphone osmTracker for Android survey in Villa Bolasco
<p>Tracks and waypoints of surveying path in Villa Bolasco and elements of interest (benches and trees)</p> <p> </p>
Lab Study Dataset: FIDO2 Platform and Roaming Authentication on Smartphones
<p>This record contains the <strong>lab study dataset and evaluation R source code</strong> from the paper "FIDO2 the Rescue? Platform vs. Roaming Authentication on Smartphones" by Leon Würsching*, Florentin Putz* <em>(* = equal contribution)</em>, Steffen Haesler, and Matthias Hollick <em> </em>in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23).</p> <p>Our pseudonymous <strong>dataset</strong> contains 22 variables for each of our 87 participants in our between-groups lab study. The variables consist of usability and acceptance scores, the adoption likelihood for 11 account types, and 9 control variables including the level of privacy concerns and ATI.</p> <p>Our R Markdown <strong>source code</strong> includes the full reproducible code of our study. This code generates all statistical figures from our paper. The code can also be used to reproduce our quantitative results and tables.</p> <p>Please refer to the README.md file and our paper for further details about the dataset and the lab study.</p> <p> </p> <p>This work has been co-funded by the LOEWE initiative (Hesse, Germany) within the emergenCITY center and the Federal Ministry of Education and Research of Germany in the project Open6GHub (grant number: 16KISK014).</p>
Identification of hybrids between the Japanese giant salamander and Chinese giant salamander using deep learning and smartphone images
<p>Biological invasions are recognized as one of the factors causing biodiversity loss. Incomplete reproductive isolation with a closely related species can result in hybridization when a non-native species is introduced into a new habitat. Management of hybrids is essential for biodiversity conservation; however, the distinction between the two species becomes a challenge in cases of hybrids with similar characteristics to native species. Although image recognition technology can be a powerful tool for identifying hybrids, studies have yet to utilize deep learning approaches. Hence, this study aimed to identify hybrids between native Japanese giant salamanders (<em>Andrias japonicus</em>) and non-native Chinese giant salamanders (<em>Andrias davidianus</em>) using EfficientNet and smartphone images. We used smartphone images of 11 native individuals (with 5 training and 6 test images) and 20 hybrid individuals (with 5 training and 15 test images). In our experimental environment, an AI model constructed with efficientNet-V2 showed 100% accuracy in identifying hybrids. In addition, highlighting the regions that influenced the AI model's predictions using Grad-CAM revealed that salamander head spots are responsible for correctly classifying native and hybrid species. The results of this study revealed that our approach is one of the methods that enable the identification of hybrids, which was previously considered difficult without identification by the experts. Furthermore, since this study achieved high-performance identification using smartphone images, it is expected to be applied to a wide range of low-cost identification using citizen science.</p>
Brightness vs t of a LED connected to a RC circuit measured using a smartphone light meter
<p>Brightness vs t of a LED connected to a RC circuit measured using the light meter of a "Xiaomi Redmi Note 8T" smartphone.</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.