53 skills found · Page 1 of 2
kaixxx / NoScribeCutting edge AI technology for automated audio transcription. A nice GUI for OpenAIs Whisper and pyannote (speaker identification)
jasonwinning / HypernomiconHypertext-infused personal research productivity/database software (Mac/Win/Linux)
chrisgrieser / Obsidian QuadroObsidian Plugin for social-scientific Qualitative Data Analysis (QDA). An open alternative to MAXQDA and atlas.ti, using Markdown to store data and research codes.
remram44 / TaguetteFree and open source qualitative research tool -- MIRROR OF GITLAB REPOSITORY
dermatologist / Nlp QrmineQualitative Research support tools in Python
Gamma-Software / Llm Qualitative Data AnalysisQualitative Data Analysis done by AI (or LLMs). 🖥️ Streamlit & 🔗 Langchain
KindOPSTAR / QualiGPTQualiGPT: An easy-to-use tool for qualitative research
pangaea-data-publisher / QualianonQualiAnon is a tool to support the anonymization of text data. It is developed by the Qualiservice research data center for the anonymization of interviews in the qualitative social sciences for archival.
nealcaren / Social Data AnalysisClaude Code plugins for quantitative and qualitative sociological research
worldbank / IQualiQual is a package that leverages natural language processing to scale up interpretative qualitative analysis. It also provides methods to assess the bias, interpretability and efficiency of the machine-enhanced codes. iQual has been applied to analyse interviews on parents' aspirations for their children in Cox's Bazaar, Bangladesh.
RE-QDA / RequalFree and open-source software for computer-assisted qualitative data analysis
cactool / CactoolAn easy way to collaboratively code social media posts for manual content and discourse analysis
muctadir / LamaLaMa, short for Labelling Machine, is an web application developed for aiding in thematic analysis of qualitative data.
ianarawjo / SplatSplat: Affinity diagramming tool in a single HTML file. Cluster notes on a board. Organize data visually. Fully local for privacy. Semantic search and AI assistance available.
nmalkin / QcasQualitative Coding Assistant for Google Sheets
akaalias / Algorand BalletA qualitative analysis tool for the Algorand blockchain
identity-research-lab / Tmi Webtmi-web is a social science research tool for managing, analyzing, coding, and visualizing qualitative survey data on identities. It presents identity and experience in a network graph, encouraging tactile exploration of intersectional identities and facets of privilege and marginalization.
Jonathandeventer / Master Thesis DCDTIn recent years, community detection has received increased attention thanks to its wide range of applications in many fields. While at first most techniques were focused on discovering communities in static networks, lately the research community’s focus has shifted toward methods that can detect meaningful substructures in evolving networks because of their high relevance in real-life problems. This thesis explores the current availability of empirical comparative studies of dynamic methods and also provides its own qualitative and quantitative comparison with the aim of gaining more insight in the performance of available algorithms that are expected to perform well in the context of social community detection. The qualitative comparison includes 13 algorithms, namely D-GT, Extended BGL, TILES, AFOCS, HOCTracker, OLCPM, DOCET, LabelRankT, FacetNet, DYNMOGA, DEMON and iLCD. The empirical analysis compares TILES, HOCTracker, OLCPM, DEMON and iLCD on synthetic RDyn graphs and the real graph, DBLP. In addition to the results of the empirical and qualitative results of the analysis, the thesis’s value lies in its wide coverage of the dynamic community detection problem.
marcgarnica13 / Ml Interpretability European FootballUnderstanding gender differences in professional European football through Machine Learning interpretability and match actions data. This repository contains the full data pipeline implemented for the study *Understanding gender differences in professional European football through Machine Learning interpretability and match actions data*. We evaluated European male, and female football players' main differential features in-match actions data under the assumption of finding significant differences and established patterns between genders. A methodology for unbiased feature extraction and objective analysis is presented based on data integration and machine learning explainability algorithms. Female (1511) and male (2700) data points were collected from event data categorized by game period and player position. Each data point included the main tactical variables supported by research and industry to evaluate and classify football styles and performance. We set up a supervised classification pipeline to predict the gender of each player by looking at their actions in the game. The comparison methodology did not include any qualitative enrichment or subjective analysis to prevent biased data enhancement or gender-related processing. The pipeline had three representative binary classification models; A logic-based Decision Trees, a probabilistic Logistic Regression and a multilevel perceptron Neural Network. Each model tried to draw the differences between male and female data points, and we extracted the results using machine learning explainability methods to understand the underlying mechanics of the models implemented. A good model predicting accuracy was consistent across the different models deployed. ## Installation Install the required python packages ``` pip install -r requirements.txt ``` To handle heterogeneity and performance efficiently, we use PySpark from [Apache Spark](https://spark.apache.org/). PySpark enables an end-user API for Spark jobs. You might want to check how to set up a local or remote Spark cluster in [their documentation](https://spark.apache.org/docs/latest/api/python/index.html). ## Repository structure This repository is organized as follows: - Preprocessed data from the two different data streams is collecting in [the data folder](data/). For the Opta files, it contains the event-based metrics computed from each match of the 2017 Women's Championship and a single file calculating the event-based metrics from the 2016 Men's Championship published [here](https://figshare.com/collections/Soccer_match_event_dataset/4415000/5). Even though we cannot publish the original data source, the two python scripts implemented to homogenize and integrate both data streams into event-based metrics are included in [the data gathering folder](data_gathering/) folder contains the graphical images and media used for the report. - The [data cleaning folder](data_cleaning/) contains descriptor scripts for both data streams and [the final integration](data_cleaning/merger.py) - [Classification](classification/) contains all the Jupyter notebooks for each model present in the experiment as well as some persistent models for testing.
V69dxz / DearscholarDearScholar: An open-source smartphone app for longitudinal scientific qualitative and quantitative (self-report) diary, log and survey research
