During our research in generating or optimizing molecules to be drug candidates by extending deep reinforcement learning and graph neural networks algorithms, we used GEOM data [1], and we had an idea to make a dataset obtained from molecules from GEOM to predit the activity towards COVID and the drug linkeness. We calculated over 200 descriptors for the molecules using RDKit [2]. We hope you enjoy using it.
References:
<p>This dataset consists of 200 occurrences extracted from three fields of a web analytics tool over time, along with labels indicating the service availability status at that moment. The data pertains to customer accesses of a real financial institution. The columns are named with a type and a unique identifier number. The column TX_ACAO_EVT represents an action performed by the customer, such as a click, system message, or background application action. The column TX_CTGR_EVT represents the category of the action, such as an error message or a specific type of action.
The datasets in discussion present detailed records for two of the world's most cultivated crops: wheat and rice. These datasets aim to provide comprehensive insights into various environmental and soil-related factors that are traditionally considered influential in determining the yield of these crops. By analyzing these datasets, researchers, agronomists, and farmers can gain a better understanding of the interplay between different attributes and how they might impact the overall crop yield.
Wheat Dataset:
The pathology files of 194 colon cancer patients, 137 breast cancer patients, 124 gastric cancer patients, and 169 thyroid cancer patients who were referred to the healthcare facilities of Qazvin Province, Iran were examined for age, sex, surgery type, and pathological information. We collected information between 2010 and 2020.
Social Media Big Dataset for Research, Analytics, Prediction, and Understanding the Global Climate Change Trends is focused on understanding the climate science, trends, and public awareness of climate change. The use of dataset for analytics of climate change trends greatly helps in researching and comprehending global climate change trends.
The Numerical Latin Letters (DNLL) dataset consists of Latin numeric letters organized into 26 distinct letter classes, corresponding to the Latin alphabet. Each class within this dataset encompasses multiple letter forms, resulting in a diverse and extensive collection. These letters vary in color, size, writing style, thickness, background, orientation, luminosity, and other attributes, making the dataset highly comprehensive and rich.
This paper conducts a systematic bibliometric analysis in the Artificial Intelligence (AI) domain to explore privacy protection research as AI technologies integrate and data privacy concerns rise. Understanding evolutionary patterns and current trends in this research is crucial. Leveraging bibliometric techniques, the authors analyze 3,061 papers from the Web of Science (WoS) database, spanning 1994 to 2023.
This dataset presents a collection of real-world RF signals encompassing three prominent wireless communication technologies: Wi-Fi (IEEE 802.11ax), LTE, and 5G. The data aims to facilitate advanced research in spectrum analysis, interference identification, and wireless communication optimization. The signals were meticulously captured under varying conditions to ensure a broad representation of real-world scenarios, including different modulation schemes, channel conditions, and data rates.
Our video action dataset is generated using a 3D simulation program developed in Unity. Each data sample consists of a video capturing a human performing various actions. Our initial set of actions comprises a total of 10 different yoga poses: camel, chair, child's pose, lord of the dance, lotus, thunderbolt, triangle, upward dog, warrior II, and warrior III. Within each of these 10 yoga poses, there are four variations, some exhibiting more pronounced differences than others. This results in a total of 40 action types within our dataset.
Our large scale alpine land cover dataset consists of 229'535 very high-resolution aerial images (50cm) and digital elevation model (50cm) with land cover annotations produced by experts in photo-interpretration . The nine land cover types in our study area include bedrock, bedrock with grass, large blocks, large blocks with grass, scree, scree with grass, water area, forest and glacier.
