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.
43
datasets available to search
ShareScore release 0.9.0
Dataset results
43 results for “Screenshots”
Roads layer screenshot of the Kunbaja online resource
<p>Information layer "roads" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</p>
Plot layer screenshot of the Kunbaja online resource
<p>Secondary information layer "female plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</p>
Magyar plots layer screenshot of the Kunbaja online resource
<p>Secondary information layer "magyar plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</p>
SoK: Taxonomy of Attacks on Open-Source Software Supply Chains - Visualization Tool Screenshots & Selected Papers
<p>This artifact complements the paper "SoK: Taxonomy of Attacks on Open-Source Software Supply Chains", submitted at IEEE S&P 2023.</p> <p>The papers selected during the Systematic Literature Review (SLR) are presented in the CSV file.</p> <p>This screenshots display the main features of the visualization tool that allows to explore the taxonomy of attacks on OSS supply chains, as well as the related safeguards and the selected references.</p>
VisuExplore Screenshots
<p>This upload contains open-access screenshots of the VisuExplore prototype that were originally published on the website http://ieg.ifs.tuwien.ac.at/projects/VisuExplore/ in 2012.</p> <p>VisuExplore is a flexible interaction and visualization framework for an electronic health record of a single patient. It presents the development of multiple heterogeneous medical variables over time. A particular focus was set on a high level of usability by using simple and easy-to-understand visual representation methods. In addition, VisuExplore is an extensible platform for further research developments. For example, three further visual representation methods were implemented and integrated during the project: a <a href="https://web.archive.org/web/20130312180901/http://ieg.ifs.tuwien.ac.at/projects/semzoom/">semantic zoom chart</a> (based on <a href="https://web.archive.org/web/20130312180901/http://doi.acm.org/10.1145/985692.985706">[Bade et al., 2004]</a>) to combine quantitative data (e.g., body temperature) and qualitative abstraction thereof (e.g., “high fever”), a <a href="https://web.archive.org/web/20130312180901/http://ieg.ifs.tuwien.ac.at/projects/HorizonVis/">horizon graph</a> (based on <a href="https://web.archive.org/web/20130312180901/http://www.stonesc.com/Vis08_Workshop/DVD/Reijner_submission.pdf">[Reijner, 2008]</a>) for a particularly space-efficient display of large amounts of numerical variables, and a document browser.</p> <p>Figure captions:</p> <ul> <li>VisuExplore prototype with simple and easy-to-understand visual representation methods: Line plot, bar chart, event chart, and timeline chart.</li> <li>VisuExplore prototype with advanced visual representation methods: Document browser, semantic zoom chart, and horizon graph</li> <li>Interaction techniques for reading values: Mouse-over tooltip, table panel with linked selection, mouse tracker</li> <li>Customizing the line plot: (1) default setting, (2) zoom y-axis to data range, (3) extend y-axis to zero, and (4) use smaller marks and thinner lines</li> <li>Flexibility of visual representation: Four variables (1) in a horizon graph, (2) combined in a line plot, or (3) in separate line plots</li> <li>Measure tool: A metaphorical tape measure for the time interval between two items (also across multiple diagrams)</li> </ul> <p>Publications based on VisuExplore:</p> <ul> <li>Rind, A., Aigner, W., Miksch, S., Wiltner, S., Pohl, M., Turic, T., & Drexler, F. (2011). Visual Exploration of Time-Oriented Patient Data for Chronic Diseases: Design Study and Evaluation. In A. Holzinger & K.-M. Simonic (Eds.), <em>Information Quality in e-Health, Proc. USAB 2011</em> (Vol. 7058, pp. 301–320). Springer. <a href="https://doi.org/10.1007/978-3-642-25364-5_22">https://doi.org/10.1007/978-3-642-25364-5_22</a></li> <li>Pohl, M., Wiltner, S., Rind, A., Aigner, W., Miksch, S., Turic, T., & Drexler, F. (2011). Patient Development at a Glance: An Evaluation of a Medical Data Visualization. In P. Campos, N. Graham, J. Jorge, N. Nunes, P. Palanque, & M. Winckler (Eds.), <em>Human-Computer Interaction – INTERACT 2011</em> (Vol. 6949, pp. 292–299). Springer Berlin Heidelberg. <a href="https://doi.org/10.1007/978-3-642-23768-3_24">https://doi.org/10.1007/978-3-642-23768-3_24</a></li> </ul>
Screenshot case-law-app
<p>This is a screenshot of the Case Law network visualization application (https://nlesc.github.io/case-law-app/), to be used in presentations or articles.</p>
UI Logs for Screenshot and for Activity Identification
<p>These data correspond to the set of Problems used for the evaluation of the proposal <strong><em>From Screenshots to Process Models: Improving Activity Identification through Screen Text.</em></strong></p> <p>Each problem includes a User-Interaction (UI) Log that contains comprehensive process information. Accompanying the UI Log are directories with 58 screenshots from different instances of processes recorded on Odoo or Gmail interfaces and directories containing all on-screen text extracted from each screenshot using OCR. The UI Log references the screenshots and their corresponding OCR files for each row.</p> <p>Additionally, results for every execution run per Problem are available. The results directories include subdirectories, differentiated by the image/text weight in the execution, containing the expanded base UI Log with columns resulting from feature extraction and clustering. Each Problem has its summary file, providing an overview of all executions and the relevant clustering and evaluation metrics.</p> <p>A precise breakdown and description of each of the directories and files present in this assessment is provided below:</p> <p><strong>input/Problem_X-Y/ </strong>(where X represents the number of the Problem given in the article and Y its full name).</p> <ul> <li><strong>screenshots/:</strong> contains the screenshots from Odoo and Gmail interfaces. The naming pattern is <em>X_img.png</em> where X is the corresponding identifier. Each record contains up to 58 screenshots representing different activities within the instantiation of a business process.</li> <li><strong>ocr_results/:</strong> stores the results of applying OCR techniques to all screenshots for extracting all on-screen text. This directory includes a .txt file for each image, containing the extracted text. The naming pattern is similar to the previous one: <em>X_ocr.txt </em>where X is the identifier. These files are used for executions that use all on-screen text as a source along with the images.</li> <li><strong>log.csv:</strong> .csv file representing the UI Log of the process. It contains the following columns: <ul> <li><strong>id:</strong> identifier corresponding to a screenshot within the UI log.</li> <li><strong>screenshot:</strong> path to the image corresponding to the row in the UI log within <em>screenshots/.</em></li> <li><strong>timestamp:</strong> time marker for defining the sequential order of actions in the UI log</li> <li><strong>header:</strong> text string extracted from the browser’s tab.</li> <li><strong>header_txt:</strong> path to each corresponding text file within <em>ocr_results/.</em></li> <li><strong>ground_truth:</strong> label assigned to identify and group images by activity. Compared with the <em>activity_label</em> column after algorithm application to determine execution’s evaluation metrics.</li> </ul> </li> </ul> <p><strong>output/X_Y/ </strong>(where X represents an abbreviation of the Problem chosen for execution and Y specifies whether all on-screen text, browser’s tab text or image hash was used).</p> <ul> <li><strong>X_Y_a_b/</strong> (where a,b refers to image/text weight for each execution). Each directory represents one specific execution from a Problem. <ul> <li><strong>df.csv:</strong> .csv file representing the expanded UI Log with the experimentation results after the algorithm has been applied. The new columns are described below: <ul> <li><strong>combined_features:</strong> contains the features extracted per image, either individually using hash or CLIP, or, in the latter case, combining them with those from the text source used.</li> <li><strong>activity_label:</strong> represents the group assigned by the algorithm through clustering based on feature comparison.</li> <li><strong>mapped_prediction:</strong> mapping of the cluster assigned by the algorithm to the group distribution established manually by <em>ground_truth.</em></li> </ul> </li> <li><strong>summary.csv: </strong>file which contains a summary of all executions from a Problem. It contains the following information: <ul> <li><strong>exec:</strong> number indicating the row identifier.</li> <li><strong>image_weight:</strong> parameter that determines the importance of image features in the model.</li> <li><strong>text_weight:</strong> parameter that determines the importance of text features in the model.</li> <li><strong>Evaluation metrics:</strong> <ul> <li><strong>Precision:</strong> the ratio of true positives to the sum of true positives and false positives. It measures the accuracy of not labeling different activities as the same.</li> <li><strong>Recall:</strong> the ratio of true positives to the sum of true positives and false negatives. It measures the ability to identify all the activities properly grouped.</li> <li><strong>F1-Score:</strong> a harmonic mean of precision and recall. It balances the relative contribution of precision and recall equally.</li> </ul> </li> <li><strong>Clustering related metrics. </strong>(Calculated for testing purposes; in the article, our approach focuses solely on evaluation metrics). <ul> <li><strong>Silhoutte Coefficient:</strong> measures how similar is an object to its cluster compared to other clusters. Values range from -1 to 1, with higher values indicating better-defined clusters.</li> <li><strong>Davies-Bouldin Index:</strong> assesses cluster quality by comparing the distance between clusters to the size of the clusters themselves. Lower values indicate better-defined clusters.</li> <li><strong>Calinski-Harabasz Index:</strong> evaluate cluster quality by comparing the spread within clusters to the spread between clusters. Higher values mean better-defined clusters.</li> </ul> </li> </ul> </li> </ul> </li> </ul>
Fantastic Screenshots (version 3)
<p>Collaboration for testing Zenodo features.</p>
Screenshots and metadata for 214 reCAPTCHA challenges encountered between September 2022 - September 2023
Open the record for dataset details and reuse information.
Data Screenshot Tweet Korban Penipuan Berkedok Freelance
Open the record for dataset details and reuse information.
Practices of Viewing IV: Screenshot (video essay)
<p>In the background of all the films I watch there are these screenshots that keep accumulating on my hard drive. Hundreds, thousands. When I see them I think of all the difficulties we once had with trying to obtain these images hidden within a film. And how easy it is today. And it makes me dream about what this images could be doing together, without my knowledge.</p>
GUI Screenshots EV reservation app
<p>The PDF document contains the screenshots of the developed prototype app to demonstrate the applicability of the reservation system for distributing the EV station utilization. It belongs to the article "How to distribute charging requests of electronic vehicles? A reservation-based approach".</p>
Screenshot Wahlwerbepost "Parteilichkeit" 2024 Liberale Hochschulgruppe Würzburg
<p>Screenshot des Posts der liberalen Hochschulgruppe zur langfristigen Nachvollziehbarkeit etwaiger darauf bezogener Anträge und Ausarbeitungen.</p>
Code and data for "ActivityGen: Extracting Enabled Activities from Screenshots"
<p>The code and data for the paper "ActivityGen: Extracting Enabled Activities from Screenshots" is provided here. </p> <p><strong>Abstract</strong></p> <p>Many tasks in organizations are performed in a desktop environment. It is possible to record users' interactions in a desktop environment by taking screenshots when an action happens. The result is an interaction log. By considering the associated images of a record, it is possible to detect which activity was performed and which activities were enabled. This information can be extracted, resulting in a translucent event log. Such a translucent event log is valuable and can be used as input for dedicated process-mining techniques. The results can be used to analyze human-computer interactions or create bots for robotic process automation. However, current techniques for extracting information on enabled activities rely on template matching, which is rigid and sensitive to variations. To solve this issue, we present our modular framework, ActivityGen. ActivityGen detects and labels graphical user interface elements by also considering additional information. ActivityGen uses more advanced techniques to overcome the limitations of previous approaches and can extract information without a user's input. Furthermore, it can be adjusted to a user's needs. It detects graphical user interface elements more accurately than state-of-the-art techniques and labels them faster, more robust, and more domain-oriented than state-of-the-art techniques.</p> <p><strong>Data</strong></p> <p>ReDraw_CLS and ReDraw_ViSM are specified in the work. </p> <p>The basis for ReDraw_CLS is the ReDraw dataset. We focus on the following components: Button, CheckBox, EditText, Image, ImageButton (which we refer to as icon), RadioButton, and Switch. We noticed that the examples of ImageView and ImageButton are similar, primarily consisting of icon images. Therefore, we removed the ImageView class and introduced an Image class instead. The Image class contains images from the validation set of the Coco validation set 2017 and the YouTube Thumbnails dataset, enabling the detection of general website images.</p> <p>ReDraw_ViSM iterates add 6,000 synthetically created buttons to the former dataset by distributing them in the same ratio into train, test, and validation sets.</p> <p>lm_basic and lm_extended contain the text training for the language models. </p> <p><strong>Code</strong></p> <p>The code allows for the execution of ActivityGen. Moreover, we provide our evaluation scripts. However, the models do not have to be trained. The models' weights are provided in the model folder.</p>
360-degree video clip of static panoramic screenshots of the AVA360VR interface that enables an analyst to inhabit video data
<p>This equirectangular 360° video clip comprises a sequence of static panoramic screenshots of the AVA360VR interface that enables an analyst to inhabit the video recorded in a camerawork training session. The screenshots are high-resolution image captures of the complete field of view triggered live by the user inside the head-mounted display from whatever position they desire (and stored to disk). The screenshot image function and the live screen capture video function allow others not in virtual reality to view the results of the use of the AVA360VR tool at a later date.</p> <p><em>AVA360VR </em>(Annotate, Visualise, Analyse 360° video in VR) is a VR software tool developed by the BigSoftVideo team at Aalborg University. The aim is to support ‘inhabiting’ 360-degree video data – that is, to explore complex spatial video and audio recordings of a scene in which social interaction took place through a tangible interface in virtual reality.</p> <p>To play the video and spatial sound correctly, use a digital video player that can play equirectangular videos with the YouTube ambiX First Order Ambisonic audio format, eg. VLC or PotPlayer. Please wear headphones.</p> <p>At the end of the clip is a short demonstration of ambisonic spatial audio.</p>
Wikipedia Screenshot dataset
Open the record for dataset details and reuse information.
Wikipedia Screenshot dataset
Open the record for dataset details and reuse information.
IGV screenshot for manual validation of 60 reported false positive small CNVs in SurVIndel2
Open the record for dataset details and reuse information.
Figure S1. Screenshots of the LIFE4YOUth intervention
<p>Supplementary Materials</p>
Features layer screenshot of the Kunbaja online resource
<p>Information layer "features" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</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.