Dataset

This dataset was created by David

Contents

Dataset

This dataset was created by Nikhil

Released under MIT

Contents

Explore the historical Whois records related to deepdata.guru (Domain). Get insights into ownership history and changes over time.

This dataset focuses on predicting drug–drug interaction (DDI) risks using machine learning techniques. It contains detailed information about various drugs, their pharmacological properties, molecular structures, and known interactions. The primary goal is to enable the development of predictive models that can identify potential adverse interactions between medications before they occur.

Key Features:

• Drug identifiers, names, and molecular descriptors
• Known drug–drug interaction pairs and severity levels
• Pharmacological classes and target information
• Features suitable for supervised learning (classification/regression)

Applications:

• Predicting harmful drug combinations in healthcare
• Enhancing clinical decision support systems
• Supporting AI-driven pharmacovigilance research

Explore the historical Whois records related to deepdata.top (Domain). Get insights into ownership history and changes over time.

CeBed is a benchmark for deep data-driven OFDM channel estimation.

데이터 개요 : * The data for extracting metadata from PDF domestic papers. * The data contains information in layout box extracted from each PDF paper with labels corresponding to metadata field types. * The information in each layout box are unique code, text, coordinates(x0, y0, x1, y1) of box, width of box, height of box and font size. * The file named as “train.txt” was constructed through the fully automatic inspection process. It contains a total of 5,241,746 labeled layout boxes for 295,306 papers in 503 journals. It was used as train set. * The file named as “valid.txt” was developed through the manual inspection process by several annotators. It contains a total of 155,629 labeled layout boxes for 9,895 papers in 503 journals. * The file named as “test.txt” was built through the manual inspection process. It contains a total of 159,925 labeled layout boxes for 10,119 papers in 503 journals. It was used as test set.데이터 설명 : The data for extracting metadata from PDF domestic papers.

데이터 개요 : * The corpus for extracting metadata from multilingual journal references. * The corpus contains the tokens of reference string with IOB labels corresponding to metadata field types. * The file named as “automatic_inspection_data(train_set).txt” was constructed through the fully automatic inspection process. It contains a total of 3,680,620 labeled references, and was used as training set for training our BERT-based parsing model in our study. * The file named as “manual_insepction_data_(validation_set).txt” was developed through the manual inspection process by several annotators. It contains a total of 63,878 labeled references, and was used as validation set. * The file named as “manual_inspection_data_(test_set).txt” was built through the manual inspection process. It contains a total of 71,489 labeled references, and was used as test set. * In the files, each reference is separated by a newline character. In each first line of references, a unique identifier and language type are listed. 데이터 설명 : The corpus for extracting metadata from multilingual journal references

Glioblastoma (GBM) is a primary brain cancer with an abysmal prognosis with few effective therapies. The ability to investigate the tumor microenvironment before and during treatment would greatly enhance both our understanding of disease response and progression, as well as the delivery and impact of therapeutics. Stereotactic biopsies are a routine surgical procedure performed primarily for diagnostic histopathologic purposes. The adaptation of stereotactic biopsy tissue for complex and integrated investigative multi-modal molecular analyses (‘Multi-omics”) in the context of GBM regional heterogeneity is not routinely performed, Most of the tissue is consumed with standard of care clinical testing and the amount and quality of remaining cells, particularly in the context of recurrent GBMs that have failed previous treatments, has not previously been shown to be amenable to complex analyses. Here we performed highly resolved multi-modal analysis methods including single cell RNA sequencing, spatial-transcriptomics, metabolomics, proteomics, phosphoproteomics, T-cell clonal analysis, and immunopeptidomics on needle biopsy cores obtained from a single patient during the same procedure. In a second patient, we analyzed multi-regional core biopsies to decipher spatially associated tissue and genomic variance. Finally in a separate cohort of patients we investigated the utility of stereotactic biopsies as a method for generating patient derived xenograft models. Dataset integration across modalities showed good correspondence between spatial modalities and revealed tumor and immune cell associated metabolic profiles and cell signalling pathways. In conclusion, stereotactic needle biopsy cores are of sufficient quality for the purposes of investigative biopsy and can generate multi-omics data, providing data rich insight into a patient’s disease to interrogate the tumor immune microenvironment.

This dataset provides a rich snapshot of GitHub users from India, capturing various aspects of their public profiles. It's a valuable resource for analyzing trends in coding activity, repository management, and user engagement within the Indian developer community. Whether you're interested in exploring how developers grow their followers, examining language preferences, or identifying patterns in contributions and achievements, this dataset offers multiple points of analysis.

Key Features: - Username: GitHub usernames of the individuals. - Gender Pronoun: Preferred gender pronouns (if available). - Followings: Number of people each user follows. - Joining Year: The year they joined GitHub. - Contributions: Number of contributions made in the last year. - Achievements: Number of GitHub achievements unlocked by the user. - Stars: Total number of stars on their repositories. - Repositories: Number of repositories created. - Followers: Number of followers each user has. - Location: User location details, primarily from India. - Languages: Primary programming language used by the individual. - Social Links: Links to their other social platforms (LinkedIn, personal websites, etc.). - Sorting Type: Categorized based on followers, repositories, or recent joining.

This dataset can be used for: - Profiling the Indian developer community. - Tracking open-source contributions and achievements. - Analyzing programming language preferences and repository management. - Exploring the relationship between social followings and coding contributions.

Perfect for data science, social network analysis, and open-source research.