⭐ Please download the dataset from here.

  PRIMUS: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Training





  🤗 Primus-FineWeb

The Primus-FineWeb dataset is constructed by filtering cybersecurity-related text from FineWeb, a refined version of Common Crawl. We began by leveraging Primus-Seed, a high-quality dataset of manually curated cybersecurity text, as positive samples. We then sampled ten times the amount of data from FineWeb as negative samples… See the full description on the dataset page: https://huggingface.co/datasets/trend-cybertron/Primus-FineWeb.

trend-db - Snapshot of the data folder

This archive is to be downloaded in the master folder of the trend-db software.

By unpacking this archive, the data/ folder is populated with the required objects to correctly run the trend-db database

The work on trend-db is described in our manuscript available at https://academic.oup.com/nar/article/49/D1/D243/5911742

doi: 10.1093/nar/gkaa722

The fashion trend forecasting service market is experiencing robust growth, driven by the increasing need for brands and retailers to stay ahead of evolving consumer preferences and optimize their product development cycles. The market's expansion is fueled by several key factors, including the rising adoption of digital technologies for trend analysis, the growing influence of social media on fashion trends, and the increasing demand for personalized and customized fashion experiences. A considerable portion of market growth stems from the integration of AI and machine learning into trend prediction tools, enabling more accurate forecasting and faster identification of emerging trends. The competitive landscape is characterized by a mix of established players like WGSN and Trendstop, along with innovative startups utilizing advanced analytics. The market is segmented by service type (e.g., trend reports, trend analysis software, consulting services), target audience (e.g., apparel brands, retailers, designers), and geography. While data scarcity prevents precise quantification, the market exhibits a dynamic interplay of established and emerging players, suggesting continued evolution and consolidation in the years ahead. The forecast period (2025-2033) is projected to witness a significant expansion driven by the continuous adoption of advanced analytics and the burgeoning demand for accurate predictions in a rapidly changing fashion landscape. Factors like globalization and increasing consumer expectations further fuel market expansion. However, challenges such as data security concerns, the need for constant innovation to stay competitive, and the potential for inaccurate forecasts pose restraints. Geographical variations in market penetration exist, with mature markets in North America and Europe gradually expanding alongside emerging markets in Asia-Pacific exhibiting higher growth potential. The competitive landscape is likely to become more concentrated with larger companies potentially acquiring smaller players to enhance their service portfolios and geographic reach. Overall, the fashion trend forecasting service market presents a compelling opportunity for players who can leverage technological advancements, build strong client relationships, and provide accurate and actionable insights.

There are two datasets related to the County Level Prevention Agenda Tracking Indicators posted on this site. Each dataset consists of county level data for 70 health tracking indicators and sub-indicators for the Prevention Agenda 2019-2024: New York State’s Health Improvement Plan. A health tracking indicator is a metric through which progress on a certain area of health improvement can be assessed. The indicators are organized by the Priority Area of the Prevention Agenda as well as the Focus Area under each Priority Area. The data sets also include indicators about major cross-cutting health outcomes and about health disparities. Each dataset includes tracking indicators for the five Priority Areas of the Prevention Agenda 2019-2024. The most recent year dataset includes the most recent county level data for all indicators. The trend dataset includes the most recent county level data and historical data, where available. Each dataset also includes the Prevention Agenda 2024 state objectives for the indicators. Sub-indicators are included in these datasets to measure health disparities among socioeconomic groups.

Mobility Trend Data

  Mobility trends as a percentage change from median value for the corresponding day of the week during the 5-week period Jan 3-Feb 6, 2020, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. Daily, up-to-date time series. See Google documentation: https://www.google.com/covid19/mobility/data_documentation.html?hl=en 
  Geography Level: State, CountyItem Vintage: Not Available
  Update Frequency: Daily/WeeklyAgency: GoogleAvailable File Type: Website link to CSV download 

  Return to Other Federal Agency Datasets Page

This dataset is a running trend analysis of runoff from USGS stream gage records from as early as 1911 to 2016 for 23 unregulated streams across the five largest Hawaiian Islands: Kauai, Oʻahu, Molokaʻi, Maui, and Hawaiʻi. First, we separated mean daily flow into direct run‐off and baseflow with the “lfstat” separation procedure in R, which employs the Institute of Hydrology (1980) standard baseflow separation procedure of 5‐day blocks to identify minimum flow, called a turning point. The turning points are then connected to obtain the baseflow hydrograph. For each stream, Sen's slope and Mann–Kendall statistic were calculated incrementally using the R package “trend” to give window sizes from 10‐107 years depending on length of the flow record. Initial mean daily stream flow data were obtained from the U.S. Geological Survey (https://waterdata.usgs.gov/nwis/rt).

Trend Micro 무역 채권자 - 현재 값, 이력 데이터, 예측, 통계, 차트 및 경제 달력 - Jul 2025.Data for Trend Micro | 무역 채권자 including historical, tables and charts were last updated by Trading Economics this last July in 2025.

This statistic shows the results of a 2017 survey in which 20 to 30 year olds in the United Kingdom (UK) were asked about how they find out about the latest high-end fashion or luxury item trends. Out of those surveyed, **** percent said that social media is the main way they find out about the latest luxury trends, making this the popular medium in the provided list.

ABOUT

The Global Population Growth Dataset provides a comprehensive record of population trends across various countries over multiple decades. It includes detailed information such as the country name, ISO3 country code, year-wise population data, population growth, and growth rate. This dataset is valuable for researchers, demographers, policymakers, and data analysts interested in studying population dynamics, demographic trends, and economic development.

Key features of the dataset:

✅ Covers multiple countries and regions worldwide
✅ Includes historical and recent population data
✅ Provides year-wise population growth and growth rate (%)
✅ Categorizes data by country and decade for better trend analysis

This dataset serves as a crucial resource for analyzing global population trends, understanding demographic shifts, and supporting socio-economic research and policy-making.

FILE INFORMATION

The dataset consists of structured records related to country-wise population data, compiled from official sources. Each file contains information on yearly population figures, growth trends, and country-specific data. The structured format makes it useful for researchers, economists, and data scientists studying demographic patterns and changes. The file type is CSV.

COLUMNS DESCRIPTION

• Country – The name of the country.
• ISO3 – The three-letter ISO code of the country.
• Year – The year corresponding to the population data, useful for trend analysis.
• Population – The total population of the country for the given year.
• Population Growth – The absolute increase in population compared to the previous year.
• Growth Rate (%) – The percentage change in population compared to the previous year.
• Decade – The decade classification (e.g., 1990s, 2000s) for grouping long-term trends.

Graph and download economic data for Leading Indicators OECD: Leading indicators: CLI: Trend restored for Finland (FINLOLITOTRSTSAM) from Jan 1985 to Aug 2022 about Finland and leading indicator.

Trend Micro reported JPY13.47B in EBIT for its fiscal quarter ending in June of 2025. Data for Trend Micro | 4704 - Ebit including historical, tables and charts were last updated by Trading Economics this last September in 2025.

Access North America Trend brand Industry Overview which includes North America country analysis of (United States, Canada, Mexico), market split by Type, Application

Consumption Trend Index (CTI): Nominal data was reported at 115.575 2020=100 in Feb 2025. This records an increase from the previous number of 115.372 2020=100 for Jan 2025. Consumption Trend Index (CTI): Nominal data is updated monthly, averaging 102.015 2020=100 from Jan 2002 (Median) to Feb 2025, with 278 observations. The data reached an all-time high of 115.575 2020=100 in Feb 2025 and a record low of 92.429 2020=100 in May 2020. Consumption Trend Index (CTI): Nominal data remains active status in CEIC and is reported by Statistical Bureau. The data is categorized under Global Database’s Japan – Table JP.H084: Consumption Trend Index: 2020=100.

U.S. Geological Survey scientists, funded by the Climate and Land Use Change Research and Development Program, developed a dataset of 2006 and 2011 land use and land cover (LULC) information for selected 100-km2 sample blocks within 29 EPA Level 3 ecoregions across the conterminous United States. The data was collected for validation of new and existing national scale LULC datasets developed from remotely sensed data sources. The data can also be used with the previously published Land Cover Trends Dataset: 1973-2000 (http:// http://pubs.usgs.gov/ds/844/), to assess land-use/land-cover change in selected ecoregions over a 37-year study period. LULC data for 2006 and 2011 was manually delineated using the same sample block classification procedures as the previous Land Cover Trends project. The methodology is based on a statistical sampling approach, manual classification of land use and land cover, and post-classification comparisons of land cover across different dates. Landsat Thematic Mapper, and Enhanced Thematic Mapper Plus imagery was interpreted using a modified Anderson Level I classification scheme. Landsat data was acquired from the National Land Cover Database (NLCD) collection of images. For the 2006 and 2011 update, ecoregion specific alterations in the sampling density were made to expedite the completion of manual block interpretations. The data collection process started with the 2000 date from the previous assessment and any needed corrections were made before interpreting the next two dates of 2006 and 2011 imagery. The 2000 land cover was copied and any changes seen in the 2006 Landsat images were digitized into a new 2006 land cover image. Similarly, the 2011 land cover image was created after completing the 2006 delineation. Results from analysis of these data include ecoregion based statistical estimates of the amount of LULC change per time period, ranking of the most common types of conversions, rates of change, and percent composition. Overall estimated amount of change per ecoregion from 2001 to 2011 ranged from a low of 370 km2 in the Northern Basin and Range Ecoregion to a high of 78,782 km2 in the Southeastern Plains Ecoregion. The Southeastern Plains Ecoregion continues to encompass the most intense forest harvesting and regrowth in the country. Forest harvesting and regrowth rates in the southeastern U.S. and Pacific Northwest continued at late 20th century levels. The land use and land cover data collected by this study is ideally suited for training, validation, and regional assessments of land use and land cover change in the U.S. because it is collected using manual interpretation techniques of Landsat data aided by high resolution photography. The 2001-2011 Land Cover Trends Dataset is provided in an Albers Conical Equal Area projection using the NAD 1983 datum. The sample blocks have a 30-meter resolution and file names follow a specific naming convention that includes the number of the ecoregion containing the block, the block number, and the Landsat image date. The data files are organized by ecoregion, and are available in the ERDAS Imagine (.img) format. U.S. Geological Survey scientists, funded by the Climate and Land Use Change Research and Development Program, developed a dataset of 2006 and 2011 land use and land cover (LULC) information for selected 100-km2 sample blocks within 29 EPA Level 3 ecoregions across the conterminous United States. The data was collected for validation of new and existing national scale LULC datasets developed from remotely sensed data sources. The data can also be used with the previously published Land Cover Trends Dataset: 1973-2000 (http:// http://pubs.usgs.gov/ds/844/), to assess land-use/land-cover change in selected ecoregions over a 37-year study period. LULC data for 2006 and 2011 was manually delineated using the same sample block classification procedures as the previous Land Cover Trends project. The methodology is based on a statistical sampling approach, manual classification of land use and land cover, and post-classification comparisons of land cover across different dates. Landsat Thematic Mapper, and Enhanced Thematic Mapper Plus imagery was interpreted using a modified Anderson Level I classification scheme. Landsat data was acquired from the National Land Cover Database (NLCD) collection of images. For the 2006 and 2011 update, ecoregion specific alterations in the sampling density were made to expedite the completion of manual block interpretations. The data collection process started with the 2000 date from the previous assessment and any needed corrections were made before interpreting the next two dates of 2006 and 2011 imagery. The 2000 land cover was copied and any changes seen in the 2006 Landsat images were digitized into a new 2006 land cover image. Similarly, the 2011 land cover image was created after completing the 2006 delineation. Results from analysis of these data include ecoregion based statistical estimates of the amount of LULC change per time period, ranking of the most common types of conversions, rates of change, and percent composition. Overall estimated amount of change per ecoregion from 2001 to 2011 ranged from a low of 370 square km in the Northern Basin and Range Ecoregion to a high of 78,782 square km in the Southeastern Plains Ecoregion. The Southeastern Plains Ecoregion continues to encompass the most intense forest harvesting and regrowth in the country. Forest harvesting and regrowth rates in the southeastern U.S. and Pacific Northwest continued at late 20th century levels. The land use and land cover data collected by this study is ideally suited for training, validation, and regional assessments of land use and land cover change in the U.S. because it’s collected using manual interpretation techniques of Landsat data aided by high resolution photography. The 2001-2011 Land Cover Trends Dataset is provided in an Albers Conical Equal Area projection using the NAD 1983 datum. The sample blocks have a 30-meter resolution and file names follow a specific naming convention that includes the number of the ecoregion containing the block, the block number, and the Landsat image date. The data files are organized by ecoregion, and are available in the ERDAS Imagine (.img) format.

The seconded quarter ended 2024 with cyclopentane priced at 1833 USD/MT in June. Demand for refrigerants increased in international as well as domestic markets during summer. Although manufacturing levels were generally high, output was inconsistent on account of periodic shutdowns in certain regions, further tightening supply.

This dataset contains aggregate data concerning the number of children who entered DCF placement during a given SFY (July 1 – June 30). These figures are broken out by the DCF Region and Office responsible for the child's care, the child's Age Group (based on age at date of entry), and the Placement Type category into which the child was initially placed.

United States CSI: Personal: HH Fin'l Situation: 5Yr Trend: Don’t Know data was reported at 3.000 % in May 2018. This stayed constant from the previous number of 3.000 % for Apr 2018. United States CSI: Personal: HH Fin'l Situation: 5Yr Trend: Don’t Know data is updated monthly, averaging 5.000 % from Feb 1979 (Median) to May 2018, with 119 observations. The data reached an all-time high of 13.000 % in Jan 1981 and a record low of 2.000 % in Sep 2017. United States CSI: Personal: HH Fin'l Situation: 5Yr Trend: Don’t Know data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H024: Consumer Sentiment Index: Personal Finance.

Relative sea level trends at US and non-US stations. The sea level trends as measured by tide gauges that are presented here are relative sea level (RSL) trends at the coastline as opposed to the global average sea level trend. Tide gauge measurements are made with respect to a local fixed reference on land. RSL change is a combination of the global sea level rise and the local vertical land motion. A negative RSL trend does not mean that the ocean is not rising. Instead, it indicates that the land is rising even faster then the ocean.The Center for Operational Oceanographic Products and Services (CO-OPS) has been measuring sea level for over 150 years, with tide stations of the National Water Level Observation Network operating on all US coasts. The information shown is also available on the CO-OPS website. https://tidesandcurrents.noaa.gov/sltrends/sltrends.htmlThe data is also available from the CO-OPS Derived Product API:https://tidesandcurrents.noaa.gov/dpapi/latest/

Graph and download economic data for Composite Leading Indicators: Composite Leading Indicator (CLI) Trend Restored for Brazil (BRALOLITOTRSTSAM) from Feb 1996 to Aug 2023 about leading indicator and Brazil.

By 2035, the Predictive Maintenance Market is estimated to expand to USD 104.65 Billion, showcasing a robust CAGR of 21.9% between 2025 and 2035, starting from a valuation of USD 11.85 Billion in 2024.