A gene and protein interactions database designed specifically for the model organism Drosophila including protein-protein, transcription factor-gene, microRNA-gene, and genetic interactions. For advanced searches and dynamic graphing capabilities the IM Browser and a DroID Cytoscape plugin are available.

This dataset consists of 361 whole slide images (WSI) - 296 malignant from women with invasive breast cancer (HER2 neg) and 65 benign. The tumours have been classified with four SNOMED-CT categories based on morphology: invasive duct carcinoma, invasive lobular carcinoma, in situ carcinoma, and others. 4144 separate annotations have been made to segment different tissue structures connected to ontologies.

The dataset consists of 101 H colon whole slide images (WSI) - 52 abnormal and 49 benign cases. All significant abnormal findings identified are outlined and categorized into 15 types such as hyperplastic polyp, high grade adenocarcinoma and necrosis. Other tissue components such as mucosa, submucosa, as well as the surgical margin are delineated to create a complete histological map. In total, 756 separate annotations have been made to segment the different tissue structures and link them to ontological information.

This dataset contains the predicted prices of the asset Droid over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Protein-protein interaction map of Notch transcription modifiers. The Notch interaction network was generated by connecting the Notch transcription modifiers identified in the genome-wide study with protein-protein interaction links (e.g. two-hybrid and Co-IP data from the DroID database []). This resulting network included 126 genes (nodes) with 237 physical interactions (edges). Genetic interactions were not used for the network and the resulting map was drawn using Cytoscape []. A. These physical links are shown in relation to components of the activated Notch pathway (N and Su(H)) and the Notch repressor complex (Su(H), H, CtBP and gro), shown in red. B. Expanded view of the chromatin factors identified in this study that form the central core of the interaction network (blue). C. Ttk is a known downstream target of Notch signaling. The transcriptional and physical interaction data suggests that this factor may have a positive feedback role in Notch induced transcription. D. Factors with roles in mRNA processing (yellow). The interaction network suggests that these proteins may be working though the chromatin machinery to modulate Notch transcription. E. The interaction network suggests the possibility of a similar chromatin based mechanism for the class of ribosomal proteins known as Minute. The network file is included with the supplemental data (Additional file ) and can be viewed in detail using the open source Cytoscape viewer http://www.cytoscape.org.

Traffic analytics, rankings, and competitive metrics for droid-life.com as of September 2025

DroidLeaks features 292 diverse resource leak bugs in popular and large-scale open-source Android apps. For each bug, DroidLeaks provides links to:
1. the code repository of the app subject
2. the concerned resource class
3. the buggy code revision (and buggy file and method names)
4. the bug-fixing code revision (i.e., link to the patch)
5. the bug report or the corresponding pull request for patches (if located)

The "Android Malware Detection Dataset" is a comprehensive collection of data designed to facilitate research in the detection and analysis of malware targeting the Android platform. This dataset encompasses a wide range of features extracted from Android applications, providing valuable insights into their behaviors and functionalities.

Key features of the dataset include:

• Permission Features: Various permissions requested by Android applications, such as access to location (coarse and fine), camera, microphone, contacts, SMS, calendar, storage, and more.
• System Features: Features related to system functions and controls, including access to device hardware (e.g., sensors, Bluetooth, NFC), system settings (e.g., changing network state, WiFi settings), and system services (e.g., managing accounts, managing documents).
• Security-related Features: Features related to security functionalities and behaviors, encompassing permission management, authentication, encryption (e.g., cryptographic operations), and security policy enforcement.
• Communication Features: Features related to communication functionalities, including sending and receiving SMS messages, making phone calls, accessing network state, and managing network connections.
• Data Access Features: Features related to accessing and manipulating data, such as reading and writing to various data sources (e.g., external storage, databases), accessing user information (e.g., contacts, call logs), and accessing app-specific data.
• App Lifecycle Features: Features related to managing the application lifecycle, including app installation and uninstallation, app startup and shutdown, app updates, and app permissions.
• Device Control Features: Features related to controlling device behavior and settings, such as changing system settings, modifying audio settings, controlling device display, and managing device power.
• Miscellaneous Features: Other miscellaneous features including accessing system logs, system services and components (e.g., camera, location manager), handling system events (e.g., incoming calls, boot completed), and interacting with system UI components.

This dataset provides researchers with a rich source of information to develop and evaluate effective malware detection and analysis techniques, ultimately contributing to the enhancement of mobile security on the Android platform.

This dataset contains the predicted prices of the asset R2D2 base droid over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

MIDI CC & NRPN details for Abildgard Droid-3 from midi.guide, the open and 'comprehensive' MIDI dataset.

Android continues to shape the mobile world. From billions of users to the latest AI enhancements, its relevance spans industries and individual lives alike. For example, Android powers mission-critical earthquake alert systems in regions with minimal infrastructure, turning everyday phones into early-warning sensors. In another use case, retail chains leverage...

The amount of Android apps available for download is constantly increasing, exerting a continuous pressure on developers to publish outstanding apps. Google Play (GP) is the default distribution channel for Android apps, which provides mobile app users with metrics to identify and report apps quality such as rating, amount of downloads, previous users comments, etc. In addition to those metrics, GP presents a set of top charts that highlight the outstanding apps in different categories. Both metrics and top app charts help developers to identify whether their development decisions are well valued by the community. Therefore, app presence in these top charts is a valuable information when understanding the features of top-apps. In this paper we present Hall-of-Apps, a dataset containing top charts' apps metadata extracted (weekly) from GP, for 4 different countries, during 30 weeks. The data is presented as (i) raw HTML files, (ii) a MongoDB database with all the information contained in app's HTML files (e.g., app description, category, general rating, etc.), and (iii) data visualizations built with the D3.js framework. A first characterization of the data along with the urls to retrieve it can be found in our online appendix: https://thesoftwaredesignlab.github.io/hall-of-apps-tools/

This dataset consists of apps needed permissions during installation and run-time. We collect apps from three different sources google play, third-party apps and malware dataset. This file contains more than 5,00,000 Android apps. features extracted at the time of installation and execution. One file contains the name of the features and others contain .apk file corresponding to it extracted permissions and API calls. Benign apps are collected from Google's play store, hiapk, app china, Android, mumayi , gfan slideme, and pandaapp. These .apk files collected from the last three years continuously and contain 81 distinct malware families.

This dataset consists of 174 WSI ovary whole slide images (WSI): 158 malignant and 16 benign. Eight of the most common, histological definable tumour types were annotated: high grade serous carcinoma (HGSC), low grade serous carcinoma (LGSC), clear cell carcinoma (CC), endometrioid adenocarcinoma (EN), metastastic serous carcinoma (MS), metastatic other (MO), serous borderline tumor (SB) and mucinous borderline tumor (MB). Also normal ovarian tissue were annotated. 2402 separate annotations were made. For the benign structures only the epithelial structures, stroma and support tissue were annotated.

Realtime Earth Satellite object tracking and orbit data for DROID-002. NORAD Identifier: 63223.

This dataset contains 18,850 normal android application packages and 10,000 malware android packages which are used to identify the behaviour of malware application on permission they need at run-time.

This dataset is about book subjects. It has 4 rows and is filtered where the books is Enterprise Android : programming Android database applications for the enterprise. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

- About Dataset

• Description
  The Data Set was downloaded from Kaggle, from the following link

• Context
  Google PlayStore Android App Data. (2.3 Million+ App Data) Backup repo: https://github.com/gauthamp10/Google-Playstore-Dataset

• Content
  I've collected the data with the help of Python script (Scrapy) running on a cloud vm instance. The data was collected in the month of june 2025.

  Also checkout:

  Apple AppStore Apps dataset: https://www.kaggle.com/gauthamp10/apple-appstore-apps Android App Permission dataset: https://www.kaggle.com/gauthamp10/app-permissions-android

• Acknowledgements
  I couldn't have build this dataset without the help of Github Education and switched to facundoolano/google-play-scraper for sane reasons

• Inspiration
  Took inspiration from: https://www.kaggle.com/lava18/google-play-store-apps to build a big database for students and researchers.

The energy consumption of Android devices, measured via data collection from features, is a recurring theme in the literature. To evaluate the performance of such devices, databases are generated by collecting data from features while using the Android operating system. This is a database generated using Tucandeira Data Collector from the daily use of smartphones and tablets while performing everyday tasks. The dataset contains 98 features and 10,331,114 records related to dynamic, background, list of applications, and static data. Device records were collected daily from ten distinct devices and stored in CSV files that were later organized to generate a database by cleaning and preprocessing the data that are publically available in the Mendeley Data Repository. The dataset formed an integral component of the SWPERFI RD&I Project, a research, development, and innovation initiative aimed at improving the performance and energy optimization of mobile devices. This project was undertaken at the Federal University of Amazonas.

We built a crawler to collect data from the Google Play store including the application's metadata and APK files. The manifest files were extracted from the APK files and then processed to extract the features. The data set is composed of 870,515 records/apps, and for each app we produced 48 features. The data set was used to built and test two bootstrap aggregating of multiple XGBoost machine learning classifiers. The dataset were collected between April 2017 and November 2018. We then checked the status of these applications on three different occasions; December 2018, February 2019, and May-June 2019.