Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
16
datasets available to search
ShareScore release 0.7.1
Dataset results
16 results for “cross-domain”
Wallhack1.8k Dataset | Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition
<p>This repository contains the <strong>Wallhack1.8k dataset</strong> for WiFi-based long-range activity recognition in Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS)/Through-Wall scenarios, as proposed in [1,2], as well as the <strong>CAD models</strong> (of 3D-printable parts) of the WiFi systems proposed in [2].</p> <p><strong>PyTroch Dataloader</strong></p> <p>A minimal PyTorch dataloader for the Wallhack1.8k dataset is provided at: <a href="https://github.com/StrohmayerJ/wallhack1.8k" target="_blank" rel="noopener">https://github.com/StrohmayerJ/wallhack1.8k</a></p> <p><strong>Dataset Description</strong></p> <p>The Wallhack1.8k dataset comprises 1,806 CSI amplitude spectrograms (and raw WiFi packet time series) corresponding to three activity classes: "no presence," "walking," and "walking + arm-waving." WiFi packets were transmitted at a frequency of 100 Hz, and each spectrogram captures a temporal context of approximately 4 seconds (400 WiFi packets).</p> <p>To assess cross-scenario and cross-system generalization, WiFi packet sequences were collected in LoS and through-wall (NLoS) scenarios, utilizing two different WiFi systems (BQ: biquad antenna and PIFA: printed inverted-F antenna). The dataset is structured accordingly:</p> <ul> <li>LOS/BQ/ <- WiFi packets collected in the LoS scenario using the BQ system</li> <li>LOS/PIFA/ <- WiFi packets collected in the LoS scenario using the PIFA system</li> <li>NLOS/BQ/ <- WiFi packets collected in the NLoS scenario using the BQ system</li> <li>NLOS/PIFA/ <- WiFi packets collected in the NLoS scenario using the PIFA system</li> </ul> <p>These directories contain the raw WiFi packet time series (see Table 1). Each row represents a single WiFi packet with the complex CSI vector <em>H</em> being stored in the "data" field and the class label being stored in the "class" field. <em>H </em>is of the form [I, R, I, R, ..., I, R], where two consecutive entries represent imaginary and real parts of complex numbers (the Channel Frequency Responses of subcarriers). Taking the absolute value of <em>H</em> (e.g., via <em>numpy.abs(H)</em>) yields the subcarrier amplitudes <em>A</em>.</p> <p>To extract the 52 L-LTF subcarriers used in [1], the following indices of <em>A</em> are to be selected:</p> <pre><code># 52 L-LTF subcarriers csi_valid_subcarrier_index = [] csi_valid_subcarrier_index += [i for i in range(6, 32)] csi_valid_subcarrier_index += [i for i in range(33, 59)]</code></pre> <p>Additional 56 HT-LTF subcarriers can be selected via:</p> <pre><code># 56 HT-LTF subcarriers csi_valid_subcarrier_index += [i for i in range(66, 94)] csi_valid_subcarrier_index += [i for i in range(95, 123)]</code></pre> <p>For more details on subcarrier selection, see <a href="https://docs.espressif.com/projects/esp-idf/en/stable/esp32/api-guides/wifi.html">ESP-IDF</a> (Section Wi-Fi Channel State Information) and <a href="https://github.com/espressif/esp-csi">esp-csi</a>.</p> <p>Extracted amplitude spectrograms with the corresponding label files of the train/validation/test split: "trainLabels.csv," "validationLabels.csv," and "testLabels.csv," can be found in the <em>spectrograms/</em> directory.</p> <p>The columns in the label files correspond to the following: [Spectrogram index, Class label, Room label]</p> <ul> <li>Spectrogram index: [0, ..., n]</li> <li>Class label: [0,1,2], where 0 = "no presence", 1 = "walking", and 2 = "walking + arm-waving."</li> <li>Room label: [0,1,2,3,4,5], where labels 1-5 correspond to the room number in the NLoS scenario (see Fig. 3 in [1]). The label 0 corresponds to no room and is used for the "no presence" class.</li> </ul> <p><strong>Dataset Overview:</strong></p> <p>Table 1: Raw WiFi packet sequences.</p> <table> <tbody> <tr> <td><strong>Scenario</strong></td> <td><strong>System</strong></td> <td><em>"no presence" / label 0</em></td> <td><em>"walking" / label 1</em></td> <td><em>"walking + arm-waving" / label 2</em></td> <td><strong>Total</strong></td> </tr> <tr> <td>LoS</td> <td>BQ</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>LoS</td> <td>PIFA</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>BQ</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>PIFA</td> <td>b1.csv</td> <td>w1.csv, w2.csv, w3.csv, w4.csv and w5.csv</td> <td>ww1.csv, ww2.csv, ww3.csv, ww4.csv and ww5.csv</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>4</td> <td>20</td> <td>20</td> <td><strong>44</strong></td> </tr> </tbody> </table> <p>Table 2: Sample/Spectrogram distribution across activity classes in Wallhack1.8k.</p> <table> <tbody> <tr> <td><strong>Scenario</strong></td> <td><strong>System</strong></td> <td> <p><em>"no presence" / </em> label 0</p> </td> <td> <p><em>"walking"</em> / label 1</p> </td> <td><em>"walking + arm-waving" / </em>label 2</td> <td><strong>Total</strong></td> </tr> <tr> <td>LoS</td> <td>BQ</td> <td>149</td> <td>154</td> <td>155</td> <td> </td> </tr> <tr> <td>LoS</td> <td>PIFA</td> <td>149</td> <td>160</td> <td>152</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>BQ</td> <td>148</td> <td>150</td> <td>152</td> <td> </td> </tr> <tr> <td>NLoS</td> <td>PIFA</td> <td>143</td> <td>147</td> <td>147</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>589</td> <td>611</td> <td>606</td> <td><strong>1,806</strong></td> </tr> </tbody> </table> <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 one of our papers [1,2].</p> <p>[1] Strohmayer, Julian, and Martin Kampel. (2024). “Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition”, <em>In IFIP International Conference on Artificial Intelligence Applications and Innovations</em> (pp. 42-56). Cham: Springer Nature Switzerland<em>,</em> doi: <a href="https://doi.org/10.1007/978-3-031-63211-2_4" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-63211-2_4</a>.</p> <p>[2] Strohmayer, Julian, and Martin Kampel., “Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition,” <em>2024 IEEE International Conference on Image Processing (ICIP)</em>, Abu Dhabi, United Arab Emirates, 2024, pp. 3594-3599, doi: <a href="https://doi.org/10.1109/ICIP51287.2024.10647666" target="_blank" rel="noopener">https://doi.org/10.1109/ICIP51287.2024.10647666</a>.</p> <p>BibTeX citations:</p> <pre>@inproceedings{strohmayer2024data, title={Data Augmentation Techniques for Cross-Domain WiFi CSI-Based Human Activity Recognition}, author={Strohmayer, Julian and Kampel, Martin}, booktitle={IFIP International Conference on Artificial Intelligence Applications and Innovations}, pages={42--56}, year={2024}, organization={Springer}}<br><br>@INPROCEEDINGS{10647666,<br> author={Strohmayer, Julian and Kampel, Martin},<br> booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, <br> title={Directional Antenna Systems for Long-Range Through-Wall Human Activity Recognition}, <br> year={2024},<br> volume={},<br> number={},<br> pages={3594-3599},<br> keywords={Visualization;Accuracy;System performance;Directional antennas;Directive antennas;Reflector antennas;Sensors;Human Activity Recognition;WiFi;Channel State Information;Through-Wall Sensing;ESP32},<br> doi={10.1109/ICIP51287.2024.10647666}}<br><br><br></pre>
Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval
<p>This submission includes all pretrained models, test data and prediction files for the EMNLP 2019 paper "Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval". Please follow the instructions in the emnlp bran at the <a href="https://github.com/castorini/birch/tree/emnlp">Birch repo</a> to reproduce the results.</p>
CrossDomainTypes4Py: A Python Dataset for Cross-Domain Evaluation of Type Inference Systems
<p>This dataset contains python repositories mined on GitHub on January 20, 2021. It allows a cross-domain evaluation of type inference systems. For this purpose, it consists of two sub-datasets, each containing only projects from the web or scientific calculation domain, respectively. Therefore we searched for projects with dependencies to either <a href="https://numpy.org/">NumPy</a> or <a href="https://flask.palletsprojects.com/en/2.0.x/">Flask</a>. Furthermore, only projects with dependencies to <a href="http://mypy-lang.org/">mypy</a> were considered, because this should ensure that at least parts of the projects have type annotations. These can be used later as ground truth. Further details about the dataset will be described in an upcoming paper, as soon as it is published it will be linked here.<br> The dataset consists of two files for the two sub-datasets. The web domain dataset contains 3129 repositories and the scientific calculation domain dataset contains 4783 repositories. The files have two columns with the URL to the GitHub repository and the used commit hash. Thus, it is possible to download the dataset using shell or python scripts, for example, the pipeline provided by <a href="https://github.com/saltudelft/many-types-4-py-dataset">ManyTypes4Py</a> can be used.<br> If repositories do not exist anymore or are private, you can contact us via the following email address: bernd.gruner@dlr.de. We have a backup of all repositories and will be happy to help you. </p>
T-SCAPE: T-cell immunogenicity scoring via cross-domain aided predictive engine
Open the record for dataset details and reuse information.
PAN19 Authorship Analysis: Cross-Domain Authorship Attribution
<p>Authorship attribution is an important problem in information retrieval and computational linguistics but also in applied areas such as law and journalism where knowing the author of a document (such as a ransom note) may enable e.g. law enforcement to save lives. The most common framework for testing candidate algorithms is the closed-set attribution task: given a sample of reference documents from a restricted and finite set of candidate authors, the task is to determine the most likely author of a previously unseen document of unknown authorship. This task may be quite challenging in <strong>cross-domain conditions</strong>, when documents of known and unknown authorship come from different domains (e.g., thematic area, genre). In addition, it is often more realistic to assume that the true author of a disputed document is not necessarily included in the list of candidates.</p> <p><strong>Fanfiction</strong> refers to fictional forms of literature which are nowadays produced by admirers ('fans') of a certain author (e.g. J.K. Rowling), novel ('Pride and Prejudice'), TV series (Sherlock Holmes), etc. The fans heavily borrow from the original work's theme, atmosphere, style, characters, story world etc. to produce new fictional literature, i.e. the so-called <strong>fanfics</strong>. This is why fanfiction is also known as transformative literature and has generated a number of controversies in recent years related to the intellectual rights property of the original authors (cf. plagiarism). Fanfiction, however, is typically produced by fans without any explicit commercial goals. The publication of fanfics typically happens online, on informal community platforms that are dedicated to making such literature accessible to a wider audience (e.g. <a href="https://www.fanfiction.net/">fanfiction.net</a>). The original work of art or genre is typically refered to as a <strong>fandom</strong>.</p> <p>This edition of PAN focuses on cross-domain attribution in fanfiction, a task that can be more accurately described as <strong>cross-fandom attribution in fanfiction</strong>. In more detail, all documents of unknown authorship are fanfics of the same fandom (target fandom) while the documents of known authorship by the candidate authors are fanfics of several fandoms (other than the target-fandom). In contrast to the PAN-2018 edition of this task, we focus on <strong>open-set attribution</strong> conditions, namely the true author of a text in the target domain is not necessarily included in the list of candidate authors.</p> <p>Each problem consists of a set of known fanfics by each candidate author and a set of unknown fanfics located in separate folders. The file <code>problem-info.json</code> that can be found in the main folder of each problem, shows the name of folder of unknown documents and the list of names of candidate author folders.</p> <p>The fanfics of known authorship belong to several fandoms (excluding the target fandom). The file <code>fandom-info.json</code> (it can be found in the main folder of each problem) provides information about the fandom of all fanfics of known authorsihp, as follows.</p> <p>The true author of each unknown document can be seen in the file <code>ground-truth.json</code>, also found in the main folder of each problem. Note that all unknown documents that are not written by any of the candidate authors belong to the <code><UNK></code> class.</p> <p>In addition, to handle a collection of such problems, the file <code>collection-info.json</code> includes all relevant information. In more detail, for each problem it lists its main folder, the language (either <code>"en"</code>, <code>"fr"</code>, <code>"it"</code>, or <code>"sp"</code>), and the encoding (always <code>UTF-8</code>) of documents.</p>
Variational Interaction Information Maximization for Cross-domain Disentanglement
<p>Cars dataset, VGG features of Sketchy dataset, and pretrained model parameters for all datasets covered in the paper.</p>
A Cross-Domain Systematic Mapping Study on Software Engineering for Digital Twins
<p><strong>A Systematic Cross-Domain Mapping Study on the Software Engineering of Digital Twins</strong></p> <p>Manuela Dalibor, Nico Jansen, Bernhard Rumpe, David Schmalzing, Louis Wachtmeister, Manuel Wimmer, and Andreas Wortmann</p> <p>Digital Twins are currently investigated as the technological backbone for providing an enhanced understanding and management of existing systems as well as for designing new systems in various domains, e.g., ranging from single manufacturing components such as sensors to large-scale systems such as smart cities. Given the diverse application domains of Digital Twins, it is not surprising that the characterization of the term Digital Twin, as well as the needs for developing and operating Digital Twins are multi-faceted. Providing a better understanding what the commonalities and differences of Digital Twins in different contexts are, may allow to build reusable support for developing, running, and managing Digital Twins by providing dedicated concepts, techniques, and tool support. In this paper, we aim to uncover the nature of Digital Twins based on a systematic mapping study which is not limited to a particular application domain or technological space. We systematically retrieved a set of 1471 unique publications of which 529 were identified as potentially relevant and of which finally 356 were selected for further investigation. In particular, we analyzed the types of research and contributions made for Digital Twins, the expected properties Digital Twins have to fulfill, how Digital Twins are realized and operated, as well as how Digital Twins are finally evaluated. Based on this analysis, we also contribute a novel feature model for Digital Twins as well as several observations to further guide future software engineering research in this area.</p>
Datasets for "A classification-based approach to override cross-domain data bias in materials discovery"
<p>This repository provides the featurized versions of the specialized datasets, SuperCon and ESTM, utilized in the study titled 'Classification-based detection and quantification of cross-domain data bias in<br>materials discovery'.</p>
Automated Support for Searching and Selecting Evidence in Software Engineering: A Cross-domain Systematic Mapping
<p>Dataset -- Brief summary of the automated approaches for searching and selecting studies for secondary studies in software engineering </p>
Data accompanying the Registered Report: Does unfairness sound wrong? A cross-domain investigation of expectations in music and social decision-making.
<p><b>This study was interested in investigating the existence of a shared psychological mechanism for the processing of expectations across domains. The literature on music and language shows that violations of expectations produce similar neural responses and violating the expectation in one domain may influence the processing of stimuli in the other domain. Like music and language, our social world is governed by a system of inherent rules or norms, such as fairness. The study therefore aimed to draw a parallel to the social domain and investigate whether a manipulation of melodic expectation can influence the processing of higher-level expectations of fairness. Specifically, we aimed to investigate whether the presence of an unexpected melody enhances or reduces participants' sensitivity to the violations of fairness and the behavioural reactions associated with these. We embedded a manipulation of melodic expectation within a social decision-making paradigm, whereby musically expected and unexpected stimuli will be simultaneously presented with fair and unfair divisions in a third-party altruistic punishment game. Both behavioural and EEG responses were recorded. Results from the pre-planned analyses show that participants are less likely to punish when the melodic stimuli are more unexpected and that violations of fairness norms elicit MFN-life effects. However, since no significant interactions between melodic expectancy and fairness of the division were found, results fail to provide evidence of a shared mechanism for the processing of expectations. Exploratory analyses show two additional effects: i) unfair divisions elicit an early attentional component (P2), likely associated with stimulus saliency, and ii) mid-value divisions elicit a late MFN-like component, likely reflecting stimulus ambiguity. Future studies could build on these results to further investigate the effect of the cross-domain influence of music on the processing of social stimuli on these early and late components.</b></p>
Figure 2 from: Güntsch A, Berendsohn W (2012) OpenUp! Creating a cross-domain pipeline for natural history data. ZooKeys 209: 47-54. https://doi.org/10.3897/zookeys.209.3179
Figure 2 - Information flow from a collection data provider via the central OpenUp! aggregator to the EUROPEANA harvester and portal. The collection database uses standard BioCASe/ABCD technology for connecting up to the network.
Figure 1 from: Güntsch A, Berendsohn W (2012) OpenUp! Creating a cross-domain pipeline for natural history data. ZooKeys 209: 47-54. https://doi.org/10.3897/zookeys.209.3179
Figure 1 - Herbarium specimen Crocus vernus L. (© Botanic Garden and Botanical Museum Berlin-Dahlem, Germany) and Tapestry called Krokus by Britta Rendahl (1976) (© Upplandsmuseet, Uppsala, Sweden).
Figure 4 from: Güntsch A, Berendsohn W (2012) OpenUp! Creating a cross-domain pipeline for natural history data. ZooKeys 209: 47-54. https://doi.org/10.3897/zookeys.209.3179
Figure 4 - OpenUp! Data Quality Toolkit annotation indicating that an identification is using a name which is a synonym (according to a concept reconciliation service provided by Kew Gardens).
Data accompanying the Registered Report: Does unfairness sound wrong? A cross-domain investigation of expectations in music and social decision-making.
Open the record for dataset details and reuse information.
Data from "Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis"
<p>This dataset includes the collected geoscientific data of<strong> lunar images, seismic data, and DAS arrays</strong>.</p> <p>It can be used to test the foundation model adaptation in the geophysical domain.</p> <p>Below are some brief desription of the datasets:</p> <p>1) Lunar images for<strong> crater detection<a href="https://moon.bao.ac.cn/">[CAS]</a></strong> : 1000 are used for training and 199 for testing, each with a size of 1022x1022.</p> <p>2) Seismic data for <strong>geobody identification<a href="https://kaggle.com/competitions/tgs-salt-identification-challenge">[TGS]</a></strong> : 3000 are used for training and 1000 for testing, each with a size of 224x224.</p> <p>3) Seismic data for <strong>facies classification<a href="https://www.aicrowd.com/challenges/seismic-facies-identification-challenge">[SEAM]</a></strong> : 250 are used for training and 45 for testing, each with a size of 1006x782.</p> <p>4) Seismic data for <strong>deep fault detection </strong>: 1081 are used for training and 269 for testing, each with a size of 896x896.</p> <p>5) DAS arrays for <strong>seismic event detection<a href="../records/8270895">[Biondi]</a></strong> : 115 are used for training and 28 for testing, each with a size of 512x512.</p> <p><strong>Tips: All the images are “float32” and the labels are "int8".</strong></p> <p>The test code has been published on GitHub: <strong><a href="https://github.com/ProgrammerZXG/Cross-Domain-Foundation-Model-Adaptation/">Cross-Domain-Foundation-Model-Adaptation</a></strong>.</p>
Figure 3 from: Güntsch A, Berendsohn W (2012) OpenUp! Creating a cross-domain pipeline for natural history data. ZooKeys 209: 47-54. https://doi.org/10.3897/zookeys.209.3179
Figure 3 - The OpenUp! Data Quality Toolkit
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.