Modified Swiss Dwellings

The Modified Swiss Dwellings (MSD) dataset is an ML-ready dataset for floor plan generation and analysis at building-level scale. The MSD dataset is completely derived from the Swiss Dwellings database (v3.0.0). The MSD dataset contains highly-detailed 5372 floor plans of single- as well as multi-unit building complexes across Switzerland, hence extending the building scale w.r.t. of other well know floor plan datasets like the RPLAN dataset.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15635478%2F9d43d7618fca2d6ebd7f99ee3009fb5f%2Foutput.png?generation=1688033406322972&alt=media" alt="">

Naming and dataset split

The naming (IDs in the folders) is based on the original dataset.

The dataset is split into train and test based on the buildings the floor plans originate from. There is, for obvious reasons, no overlap between building identities in train and test set. Hence, all floor plans that originate from the same building will be either all in the train set or all in the test set. We included as well a cleaned, filtered, and modified Pandas dataframe with all geometries (such as rooms, walls, etc.) derived from the original dataset. The unique floor plan IDs in the dataframe is the same as train and test set combined. We included it to allow users to develop their own algorithms on top of it, such as image, structure, and graph extraction.

Example use-case: Floor plan auto-completion

The MSD dataset is developed with the goal for the computer science community to develop (deep learning) models for tasks such as floor plan auto-completion. The floor plan auto-completion task takes as input the boundary of a building, the structural elements necessary for the building’s structural integrity, and a set of user constraints formalized in a graph structure, with the goal of automatically generating the full floor plan. Specifically, the goal is to learn the correlation between the the joint distribution of graph_in and struct_in with that of full_out. graph_out is provided when researchers want to use / develop methods from graph signal processing, or graph machine learning specifically. This was a challenge which was part of the 1st Workshop on Computer-Aided Architectural Design (CVAAD) - an official half-day workshop at ICCV 2023.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15635478%2F4079b2c34fc70ba5e223fa831bd14ded%2FPicture1.png?generation=1688033493295295&alt=media" alt="">

Important links

• 4TU.ResearchData
• Swiss Dwellings (dataset source)
• CVAAD (@ICCV 2023)
• GitHub guidelines

.MSD Whois Database, discover comprehensive ownership details, registration dates, and more for .MSD TLD with Whois Data Center.

The files contained here include magnetic field simulation data and code to use the data for the generation of the magnetic separation device (MSD) used for the development of the integrated basophil isolation device (i-BID). Also includes is a video demonstrating the operation of the i-BID.

Comprehensive dataset containing 11 verified MSD locations in Indonesia with complete contact information, ratings, reviews, and location data.

Access updated Msd import data India with HS Code, price, importers list, Indian ports, exporting countries, and verified Msd buyers in India.

Data for a figure that shows the scaling of uncertainty as a function of simulation length and number of atoms and step size, for both true and anitcorrelated random walks. This compares the kinisi approach (on just 32 unique walks) with the numerical values from (1024 unique walks).

Created using showyourwork from this GitHub repo.

Non-traditional data signals from social media and employment platforms for MSD stock analysis

Explore Indian Msd export data with HS codes, pricing, ports, and a verified list of Msd exporters and suppliers from India with complete shipment insights.

Medical Supplies Division Msd Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Msd And Co Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Protein-Protein, Genetic, and Chemical Interactions for MSD-3 (Caenorhabditis elegans) curated by BioGRID (https://thebiogrid.org); DEFINITION: Major Sperm protein Domain containing

Non-traditional data signals from social media and employment platforms for MSDAU stock analysis

PANTHER (PAN and other Trace Hydrohalocarbon ExpeRiment) is an in situ 6-channel gas chromatograph (GC) containing a 2-channel Mass Selective Detector (MSD) that was flown aboard the NSF/NCAR GV during HIPPO-2. The dataset contains mixing ratios in NASA Ames format.

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Abstract: Artificial Intelligence (AI) models require large volumes of data for training and testing. Data requirements present challenges for using AI to explore extreme events with limited observational data. This webinar will showcase two innovative methods developed by part of the European Climate Intelligence (CLINT) project to overcome data challenges and harness AI to improve our understanding of climate extremes. Dr. Ascenso will present his research on data augmentation methods to improve estimates of tropical cyclones using satellite data. His presentation will review established methods for data augmentation and explore opportunities and challenges for using generative AI to generate images of extreme, life-threatening tropical cyclones. Next, Dr. Plesiat will present his research on deep learning techniques to overcome limited observational data sets. His presentation will illustrate deep learning methods to develop AI reconstructions of four climate indices across Europe. Presenters : Dr. Guido Ascenso (post-doctoral researcher, Politecnico di Milano); Dr. Étienne Plésiat (German Climate Computing Centre - DKRZ) Moderator(s): Stefano Galelli (MSD CoP WG Co-Lead), David Gold (MSD CoP WG Co-Lead), Jillian Sturtevant (MSD CoP WG Communications Officer), Matteo Giuliani (Politecnico di Milano, MSDmore » CoP WG Member, Moderator and Organizer) This webinar was held on: October 11, 2024 from 11AM - 1PM ET« less

Repository for electron microscopy density maps of macromolecular complexes and subcellular structures at Protein Data Bank in Europe. Covers techniques, including single-particle analysis, electron tomography, and electron (2D) crystallography.

View Company name msd inc import data USA including customs records, shipments, HS codes, suppliers, buyer details & company profile at Seair Exim.

Data for a figure that shows the distributions of the mean and varance of the diffusion coefficient and offset that are found from the analysis of 1024 unique random walks using kinisi. This is performed for both a truely random walk and a temporally anti-correlated random walk.

Created using showyourwork from this GitHub repo.

With recent advances in machine learning, semantic segmentation algorithms are becoming increasingly general purpose and translatable to unseen tasks. Many key algorithmic advances in the field of medical imaging are commonly validated on a small number of tasks, limiting our understanding of the generalisability of the proposed contributions. A model which works out-of-the-box on many tasks, in the spirit of AutoML, would have a tremendous impact on healthcare. The field of medical imaging is also missing a fully open source and comprehensive benchmark for general purpose algorithmic validation and testing covering a large span of challenges, such as: small data, unbalanced labels, large-ranging object scales, multi-class labels, and multimodal imaging, etc. This challenge and dataset aims to provide such resource through the open sourcing of large medical imaging datasets on several highly different tasks, and by standardising the analysis and validation process.

Context

The Million Song Dataset (MSD) is a freely-available collection of audio features and metadata for a million contemporary popular music tracks. This is a subset of the MSD and contains audio features of songs with the year of the song. The purpose being to predict the release year of a song from audio features.

Content

The owners recommend that you split the data like this to avoid the 'producer effect' by making sure no song from a given artist ends up in both the train and test set.

• train: first 463,715 examples
• test: last 51,630 examples

Field descriptions:

• The first value is the year (target), ranging from 1922 to 2011.
• Then there are 90 attributes
  • TimbreAverage[1-12]
  • TimbreCovariance[1-78]

These features were extracted from the 'timbre' features from The Echo Nest API. The authors took the average and covariance over all 'segments' and each segment was described by a 12-dimensional timbre vector.

Acknowledgements

Original dataset: Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere. The Million Song Dataset. In Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), 2

Subset downloaded from: https://archive.ics.uci.edu/ml/datasets/yearpredictionmsd

Inspiration

Use this dataset to predict the years that each song was released based on it's audio features