Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Dataset of network simulator related-question posts in stack overflow.

ABSTRACT: Although the use of network simulator (NS) in predicting the behavior of computer networks has increased, the users often face a variety of challenges and share them on Stack Overflow (SO). However, the challenges that users deal with have not been studied. This paper presents an NS discussion dataset extracted from SOTorrent, which consists of 2,322 NS-related question posts spanning 17 features. The process of data collection was conducted in five steps, including filtering initial post dataset using simulator tags, discovering NS-related tags, collecting the tagged posts, extracting the posts title and preprocessing for LDA (Latent Dirichlet Allocation), and finally applying the LDA topic modeling to obtain the NS posts clustered into eight different topic names. We believe that this dataset will help research community in highlighting issues faced by NS users.

SUBMITTER: Nugroho YS

PROVIDER: S-EPMC8857582 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Dataset of network simulator related-question posts in stack overflow.

Nugroho Yusuf Sulistyo YS Islam Syful S Gunawan Dedi D Kurniawan Yogiek Indra YI Hossain Md Javed MJ

Data in brief 20220211

Although the use of network simulator (NS) in predicting the behavior of computer networks has increased, the users often face a variety of challenges and share them on Stack Overflow (SO). However, the challenges that users deal with have not been studied. This paper presents an NS discussion dataset extracted from SOTorrent, which consists of 2,322 NS-related question posts spanning 17 features. The process of data collection was conducted in five steps, including filtering initial post datase ...[more]

PMID: 35242924

Similar Datasets

Identification of mobile development issues using semantic topic modeling of Stack Overflow posts.

Project description:BackgroundIncreasing demands for mobile apps and services have recently led to an intensification of mobile development activities. With the proliferation of mobile development, there has been a major transformation in the architectures, paradigms, knowledge domains and skills of traditional software systems towards mobile development. Therefore, mobile developers experience a wide spectrum of issues specific to development processes of mobile apps and services.MethodsIn this article, we conducted a semantic content analysis based on topic modeling using mobile-related questions on Stack Overflow, a popular Q&A site for developers. With the aim of providing an understanding of the issues and challenges faced by mobile developers, we used a semi-automated methodology based on latent Dirichlet allocation (LDA), a probabilistic and generative approach for topic modeling.ResultsOur findings revealed that mobile developers' questions focused on 36 topics in six main categories, including "Development", "UI settings", "Tools", "Data Management", "Multimedia", and "Mobile APIs". Besides, we investigated the temporal trends of the discovered issues and their relationships with mobile technologies. Our findings also revealed which issues are the most popular and which issues are the most difficult for mobile development. The methodology and findings of this study have valuable implications for mobile development stakeholders including tool builders, developers, researchers, and educators.

| S-EPMC10703021 | biostudies-literature

Patterns of interest change in stack overflow.

Project description:Stack Overflow is currently the largest programming related question and answer community, containing multiple programming areas. The change of user's interest is the micro-representation of the intersection of macro-knowledge and has been widely studied in scientific fields, such as literature data sets. However, there is still very little research for the general public, such as the question and answer community. Therefore, we analyze the interest changes of 2,307,720 users in Stack Overflow in this work. Specifically, we classify the tag network in the community, vectorize the topic of questions to quantify the user's interest change patterns. Results show that the change pattern of user interest has the characteristic of a power-law distribution, which is different from the exponential distribution of scientists' interest change, but they are all affected by three features, heterogeneity, recency and proximity. Furthermore, the relationship between users' reputations and interest changes is negatively correlated, suggesting the importance of concentration, i.e., those who focus on specific areas are more likely to gain a higher reputation. In general, our work is a supplement to the public interest changes in science, and it can also help community managers better design recommendation algorithms and promote the healthy development of communities.

| S-EPMC9259656 | biostudies-literature

Is it feasible to detect FLOSS version release events from textual messages? A case study on Stack Overflow.

Project description:Topic Detection and Tracking (TDT) is a very active research question within the area of text mining, generally applied to news feeds and Twitter datasets, where topics and events are detected. The notion of "event" is broad, but typically it applies to occurrences that can be detected from a single post or a message. Little attention has been drawn to what we call "micro-events", which, due to their nature, cannot be detected from a single piece of textual information. The study investigates the feasibility of micro-event detection on textual data using a sample of messages from the Stack Overflow Q&A platform and Free/Libre Open Source Software (FLOSS) version releases from Libraries.io dataset. We build pipelines for detection of micro-events using three different estimators whose parameters are optimized using a grid search approach. We consider two feature spaces: LDA topic modeling with sentiment analysis, and hSBM topics with sentiment analysis. The feature spaces are optimized using the recursive feature elimination with cross validation (RFECV) strategy. In our experiments we investigate whether there is a characteristic change in the topics distribution or sentiment features before or after micro-events take place and we thoroughly evaluate the capacity of each variant of our analysis pipeline to detect micro-events. Additionally, we perform a detailed statistical analysis of the models, including influential cases, variance inflation factors, validation of the linearity assumption, pseudo R2 measures and no-information rate. Finally, in order to study limits of micro-event detection, we design a method for generating micro-event synthetic datasets with similar properties to the real-world data, and use them to identify the micro-event detectability threshold for each of the evaluated classifiers.

| S-EPMC7861391 | biostudies-literature

Marijuana-Related Posts on Instagram.

Project description:Instagram is a highly visual social networking site whose audience continues to grow, especially among young adults. In the present study, we examine marijuana-related content on Instagram to better understand the varied types of marijuana-related social networking occurring on this popular social media platform. We collected 417,561 Instagram posts with marijuana-related hashtags from November 29 to December 12, 2014. We assessed content of a random sample (n?=?5000) of these posts with marijuana-related hashtags. Approximately 2136 (43 %) were explicit about marijuana and further analyzed. Of the 2136 marijuana-related posts, images of marijuana were common (n?=?1568). Among these 1568 marijuana images, traditional forms (i.e., buds/leaves) were the most common (63 %), followed by some novel forms of marijuana, including marijuana concentrates (20 %). Among the 568 posts that displayed marijuana being ingested, 20 % showed someone dabbing marijuana concentrates. Marijuana-related advertisements were also observed among the 2136 marijuana-related posts (9 %). Our findings signal the promotion of marijuana use in its traditional plant-based form; trendy and novel modes of marijuana ingestion were also endorsed. This content along with the explicit marketing of marijuana that we observed on Instagram have potential to influence social norms surrounding marijuana use.

| S-EPMC4939096 | biostudies-literature

Particle stack from TRPM8 bound to calcium dataset

Project description: Not available

| EMPIAR-11233 | biostudies-other

Dataset of controversial news posts in Spanish from the reader's perspective

Project description: Not available

| S-EPMC10912592 | biostudies-literature

Dataset of depressive posts in Russian language collected from social media.

Project description:This paper presents dataset collected from social networks that are mostly used by youth of Commonwealth of Independent States (CIS) countries. The data was collected from public accounts of VKontakte social network by using VK.api and applying the most used keywords that would signify depressive mood. The collected data was classified by psychologists into two types: depressive and non-depressive. The dataset consists of 32 018 depressive posts and 32 021 non-depressive posts. Since the most common language that is spoken in CIS countries is Russian, the posts are written in Russian, consequently the collected data is in Russian language as well. The data can mostly be useful for researchers who explore tendencies to depression in CIS countries. The dataset is important for the research community, as it was not only collected from open sources, but also marked by our psychiatrists from the republican scientific and practical center of mental health. Since the dataset has very high validity, it can be used for further research in the field of mental health.

| S-EPMC7016367 | biostudies-literature

Characterizing JUUL-related posts on Twitter.

Project description:BackgroundAs vaping rapidly becomes more prevalent, social media data can be harnessed to capture individuals' discussions of e-cigarette products quickly. The JUUL vaporizer is the latest advancement in e-cigarette technology, which delivers nicotine to the user from a device that is the size and shape of a thumb drive. Despite JUUL's growing popularity, little research has been conducted on JUUL. Here we utilized Twitter data to determine the public's early experiences with JUUL describing topics of posts.MethodsTwitter posts containing the term "JUUL" were obtained for 1 April 2107 to 14 December 2017. Text classifiers were used to identify topics in posts (n = 81,689).ResultsThe most prevalent topic wasPerson Tagging (use of @username to tag someone in a post) at 20.48% followed by Pods (mentions of JUUL's refill cartridge) at 14.72% and Buying (mentions of purchases) at 10.49%. The topic School (posts indicative of using JUUL or seeing someone use JUUL while at elementary, middle, or high school) comprised 3.66% of posts. The topic of Quit Smoking was rare at 0.29%.ConclusionsData from social media may be used to extend the surveillance of newly introduced vaping products. Findings suggest Twitter users are bonding around, and inquiring about, JUUL on social media. JUUL's discreetness may facilitate its use in places where vaping is prohibited. Educators may be in need of training on how to identify JUUL in the classroom. Despite JUUL's branding as a smoking alternative, very few Twitter users mentioned smoking cessation with JUUL.

| S-EPMC6693487 | biostudies-literature

Dataset of a vanadium redox flow battery 10 membrane-electrode assembly stack.

Project description:This paper contains a vanadium redox flow battery stack with an electrode surface area 40 cm2 test data. The aim of the study was to characterize the performance of the stack of the original design. The dataset include three series of galvanostatic charge-discharge cycling in the potential region 8-16 V with current densities 75, 150 and 200 mA/cm2 for 100 cycles. Coulomb, voltaic, energy efficiencies and capacity utilization coefficient are also provided for all three series.

| S-EPMC7306588 | biostudies-literature

Development of a large-scale medical visual question-answering dataset.

Project description:BackgroundMedical Visual Question Answering (MedVQA) enhances diagnostic accuracy and healthcare delivery by leveraging artificial intelligence to interpret medical images. This study aims to redefine MedVQA as a generation task that mirrors human-machine interaction and to develop a model capable of integrating complex visual and textual information.MethodsWe constructed a large-scale medical visual-question answering dataset, PMC-VQA, containing 227,000 VQA pairs across 149,000 images that span various modalities and diseases. We introduced a generative model that aligns visual information from a pre-trained vision encoder with a large language model. This model was initially trained on PMC-VQA and subsequently fine-tuned on multiple public benchmarks.ResultsHere, we show that our model significantly outperforms existing MedVQA models in generating relevant, accurate free-form answers. We also propose a manually verified test set that presents a greater challenge and serves as a robust measure to monitor the advancement of generative MedVQA methods.ConclusionsThe PMC-VQA dataset proves to be an essential resource for the research community, and our model marks a significant breakthrough in MedVQA. We maintain a leaderboard to facilitate comprehensive evaluation and comparison, providing a centralized resource for benchmarking state-of-the-art approaches.

| S-EPMC11663219 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data