from datasets import load_dataset from IPython.display import display, HTML

Load the dataset

ds = load_dataset("vector-institute/newsmediabias-plus-clean")

Shuffle the dataset and select 50 random records

random_records = ds['train'].shuffle(seed=42).select(range(50)) # Adjust 'train' if needed

Display the first few records (e.g., article text and image if available)

for i, record in enumerate(random_records): article_text = ' '.join(record['article_text'].split()[:200]) # First… See the full description on the dataset page: https://huggingface.co/datasets/vector-institute/nmb-plus-clean.

The size and share of this market is categorized based on 2D Vector Graphics Software (Illustration Software, Animation Software, Icon Design Software, Logo Design Software, Layout Software) and 3D Vector Graphics Software (3D Modeling Software, Rendering Software, Animation Software, Simulation Software, Game Development Software) and Web-based Vector Graphics Software (Online Illustration Tools, Collaborative Design Platforms, Web-based Animation Tools, Cloud-based Design Software, SVG Editors) and geographical regions (North America, Europe, Asia-Pacific, South America, Middle-East and Africa).

Dataset Summary

Placeholder You can load the dataset via: import datasets data = datasets.load_dataset('GEM/wiki_lingua')

The data loader can be found here.

  website

None (See Repository)

  paper

https://www.aclweb.org/anthology/2020.findings-emnlp.360/

  authors

Faisal Ladhak (Columbia University), Esin Durmus (Stanford University), Claire Cardie (Cornell University), Kathleen McKeown (Columbia University)

  Dataset Overview





  Where to… See the full description on the dataset page: https://huggingface.co/datasets/vector/test_demo.

Projections indicate that the Vector Database Market will maintain a 21.7%CAGR, resulting in a market size of USD 10,409.89 million by the conclusion of 2032.

Dataset Card for TreeOfLife-10M Vector database

Persistent files for vector Database created with chromadb containing the embeddings for all images in the imageomics/TreeOfLife-10M dataset.

  Dataset Details

This dataset contains the generated vector database built using ChromaDb as the backend vector database solution for the entire TreeOfLife-10M dataset. The rationale behind creating a vector database was to enable blazingly fast nearest neighbor search. The vector… See the full description on the dataset page: https://huggingface.co/datasets/imageomics/tree-of-life-vector-db.

Global Vector Database market size is expected to reach $7.13 billion by 2029 at 23.7%, segmented as by relational vector databases, traditional relational databases with vector support, enhanced query capabilities

The lidar 10m Vector Ruggedness Measure is the primary 10m Vector Ruggedness Measure data product produced and distributed by the National Park Service, Great Smoky Mountains National Park.

Layers of geospatial data include contours, boundaries, land cover, hydrography, roads, transportation, geographic names, structures, and other selected map features.

The Kansas Tagged Vector Contour (TVC) dataset consists of digitized contours from the 7.5 minute topographic quadrangle maps. Coverage for the state is incomplete. Contour interval varies. The Kansas TVC dataset was developed to facilitate the production of the Kansas 5 and 10 foot DEM dataset. The original TVC dataset was provided by the U.S. Geological Survey and processed by the Data Access and Support Center (DASC).The full Kansas geospatial catalog is administered by the Kansas Data Access & Support Center (DASC) and can be found at the following URL: https://hub.kansasgis.org/

The Viral Vector Production (Research-Use) Market Report is Segmented by Vector Type (Adenoviral Vectors, Lentiviral Vectors, Retroviral, Vectors, and Other Types), by Application (Cell and Gene Therapy Research, Vaccine Studies, and Others), by End-User (Pharmaceutical and Biotechnology Companies and Academic Centers and Research Institutes) and Geography (North America, Europe, Asia-Pacific, Middle East and Africa, and South America). The Report Offers Market Size and Forecast for all the Above Segments in Value (USD).

This paper illustrates how to handle a sequence of extreme observations-such as those recorded during the COVID?19 pandemic-when estimating a vector autoregression, which is the most popular time-series model in macroeconomics. Our results show that the ad hoc strategy of dropping these observations may be acceptable for the purpose of parameter estimation. However, disregarding these recent data is inappropriate for forecasting the future evolution of the economy, because it may underestimate uncertainty.

HumaniBench: A Human-Centric Benchmark for Large Multimodal Models Evaluation

HumaniBench is a benchmark for evaluating large multimodal models (LMMs) using real-world, human-centric criteria. It consists of 32,000+ image–question pairs across 7 tasks:

✅ Open/closed VQA 🌍 Multilingual QA 📌 Visual grounding 💬 Empathetic captioning 🧠 Robustness, reasoning, and ethics

Each example is annotated with GPT-4o drafts, then verified by experts to ensure quality and alignment.… See the full description on the dataset page: https://huggingface.co/datasets/vector-institute/HumaniBench.

This dataset supports the Biomarker: Vector-Borne Viruses page on the Tempe Wastewater BioIntel Program site.Wastewater collection areas are comprised of merged sewage drainage basins that flow to a shared testing location for the Tempe Wastewater BioIntel Program. The wastewater collection areas represent a geographic area for which virus activity is tested. People infected with a virus excrete the virus in their feces in a process known as “shedding”. The municipal wastewater treatment system (sewage system) collects and aggregates these bathroom contributions across communities. The process begins at sampling site where, over a period of 24 hours, a wastewater sample is collected along the sewer line. After the sample is acquired, it is immediately transferred to a lab where scientists prepare the sample. The laboratory analysis seeks to determine if there is a signal (or detectable presence) of the biomarker in the wastewater. Please see the Tempe Wastewater BioIntel Program site for more information on the wastewater testing process at https://wastewater.tempe.gov/. About the data: These data illustrate a trend of the signal of the weekly average or weekly results of Tempe wastewater biomarker groups. The dashboard and collection area map do not depict the number of individuals infected. Each collection area includes at least one sampling location, which collects wastewater from across the collection area. It does not reflect the specific location where the deposit occurs. While testing can successfully quantify the results, research has not yet determined the relationship between these values and the number of people who are contributing to the signals. The influence of this data on community health decisions in the future is unknown. Data collection is being used to depict overall weekly trends and should not be interpreted without a holistic assessment of public health data. The purpose of this weekly data is to support research as well as to identify overall trends of the genome copies in each liter of wastewater per collection area. We share this information with the public with the disclaimer that only the future can tell how much “diagnostic value” we can and should attribute to the numeric measurements we obtain from the sewer. However, we know what we measure is real and we share that info with our community. Data are shared as the testing results become available. As results may not be released at the same time, testing results for each area may not yet be seen for a given day or week. The dashboard presents the weekly averages. Data are collected from 2-7 days per week. For Collection Area 1, Tempe's wastewater co-mingles with wastewater from a regional sewage line. Tempe's sewage makes up most of Collection Area 1 samples. For Collection Area 3, Tempe's wastewater co-mingles with wastewater from a regional sewage line. For analysis and reporting, Tempe’s wastewater is separated from regional sewage. Week start date represents the starting date of the testing week, which starts on Mondays and ends on Sundays. Additional Information:Source: The Translational Genomics Research Institute (TGen), part of City of Hope, is an Arizona-based, nonprofit medical research institute.Contact: Kimberly SoteloContact email: kimberly_sotelo@tempe.govPreparation Method: Initial values are provided by TGen. Tempe makes additional calculations to determine the weekly averages or weekly results for each biomarker.Publish Frequency: Weekly or as data becomes availablePublish Method: ManualData Dictionary

The full datasheet for this product is available here.The Sonoma County hydrologic data deliverables were produced in fall 2015 and winter 2016 from the countywide 2013 LiDAR data. The hydrologic products include a set of vector deliverables and a set of raster deliverables. Vector products include stream centerlines, confluence points, hydroenforcement burn locations, and watersheds. Raster products include flow direction, flow accumulation, and a hydroenforced bare earth digital elevation model (DEM). Hydroenforcement of a DEM imparts the true elevations of culverts, pipelines, and other buried passages for water into a Digital Elevation Model, creating a DEM suitable for modeling the flow of surface water.

The extent of all deliverables is all of Sonoma County, the Lake Sonoma watershed in Mendocino County, and the Lake Mendocino area. Appropriate Use: These hydrologic datasets are a mostly-automated first step in the eventual development of a 'localized' or 'LiDAR enhanced' National Hydrography Dataset (NHD). They are suitable for landscape level planning and hydrologic modeling. These data products do not contain a guarantee of accuracy or precision and – without site specific validation and/or refinement – should not be relied upon for engineering level or very fine scale decision making. Detailed Dataset Description:These hydrologic data products were produced by Quantum Spatial. Quantum Spatial used mainly automated methods to create the hydrologic data products. Quantum Spatial included a short data report with the hydrologic datasets titled Sonoma County Hydroenforcement Technical Data Report - access that report here: https://sonomaopenspace.egnyte.com/dl/nHT2fGg8TP

The individual hydrologic data products are described briefly below.

  Vector Hydro Products (contained in this file gdb):    

Stream Centerlines – Centerlines of streams in Sonoma County. An area of flow concentration is considered a stream if its flow accumulation (upstream catchment area) exceeds 5 acres and a clearly defined channel exists. Where possible, stream centerline names (GNIS_Name) are consistent with the NHD. Hydroenforcement Burn Locations - Line features that represent locations where hydroenforcement occurred. Confluence Points – Points that represent stream intersections (confluences).Watersheds (HUC2 through HUC16) – Watershed boundaries for nested hydrologic units from HUC 2 (region) to HUC 16 (eighth level sub-watershed). Where possible, watershed names are consistent with the NHD. Watershed mapping conventions follow those for NHD's Watershed Boundary Dataset (http://nhd.usgs.gov/wbd.html).

Raster Hydrologic Products (1-meter resolution - available at http://sonomavegmap.org/data-downloads)Hydroenforced Digital Elevation Model – The Hydroenforced DEM is the LiDAR derived (2013) bare earth DEM with contours, pipelines and other buried passages to water 'burned in', so that the DEM correctly models surface water flow.Flow Direction Rasters – Values in a flow direction raster represent one of eight directions (pixel values range from 1 to 8); No Data represents areas where there is no flow off of the pixel (sinks).Flow Accumulation Rasters – Flow accumulation is a measure of upstream catchment area. Pixel values in a flow accumulation raster represent the cumulative number of upstream pixels (in other words, the count of pixels that contribute flow to a given pixel).

Cadaster data from PDOK used to illustrate the use of geopandas and shapely, geospatial python packages for manipulating vector data. The brpgewaspercelen_definitief_2020.gpkg file has been subsetted in order to make the download manageable for workshops. Other datasets are copies of those available from PDOK.

Discover insights on the growth projections of the Global Viral Vector Manufacturing Market, anticipated to expand at a significant rate of approximately 19.22% CAGR from 2024 to 2030. Explore the forecasted trends and potential opportunities in this dynamic sector.

The OATH ECB Hearings Case Status dataset contains information about alleged public safety and quality of life violations that are filed and adjudicated through the City’s administrative law court, the NYC Office of Administrative Trials and Hearings (OATH) and provides information about the infraction charged, decision outcome, payments, amounts and fees relating to the case.

The global vector databases market for generative AI applications is projected to grow from an estimated USD 276 million in 2025 to a value of USD 526 million by 2033, exhibiting a CAGR of 13.6% during the forecast period. The increasing adoption of generative AI applications in natural language processing (NLP), computer vision, and other domains is driving market growth. Key market drivers include the rising demand for real-time data processing and analysis, the proliferation of IoT devices, and the growing popularity of deep learning and artificial intelligence (AI) technologies. The market is also benefitting from the increasing awareness of the advantages of vector databases, such as their ability to handle large volumes of data and their efficient and scalable performance. The major market trends include the shift towards cloud-based vector databases, the development of new and innovative solutions by vendors, and the growing number of applications in the healthcare, finance, and retail sectors.

Spatial coverage index compiled by East View Geospatial of set "Qatar 1:1,000 Scale Vector Data". Source data from QCGIS (publisher). Type: Topographic. Scale: 1:1,000. Region: Middle East.