United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

Internet use in the UK annual estimates by age, sex, disability, ethnic group, economic activity and geographical location, including confidence intervals.

IRL + Online Data

## Overview

IRL + Online Data is a dataset for object detection tasks - it contains Goggles annotations for 4,527 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

The Data Visualization Workshop II: Data Wrangling was a web-based event held on October 18, 2017. This workshop report summarizes the individual perspectives of a group of visualization experts from the public, private, and academic sectors who met online to discuss how to improve the creation and use of high-quality visualizations. The specific focus of this workshop was on the complexities of "data wrangling". Data wrangling includes finding the appropriate data sources that are both accessible and usable and then shaping and combining that data to facilitate the most accurate and meaningful analysis possible. The workshop was organized as a 3-hour web event and moderated by the members of the Human Computer Interaction and Information Management Task Force of the Networking and Information Technology Research and Development Program's Big Data Interagency Working Group. Report prepared by the Human Computer Interaction And Information Management Task Force, Big Data Interagency Working Group, Networking & Information Technology Research & Development Subcommittee, Committee On Technology Of The National Science & Technology Council...

Dataset

This dataset was created by Firda Daniel

Contents

This study contains two data files. Data file one (Broadband Internet Availability, Speed, and Adoption by Census Tract) contains measures of broadband internet availability, speed, and adoption per United States census tract in 2014 through 2020. The data is derived from internet service providers' Form 477 reports to the Federal Communications Commission. Data file two (Broadband Internet Availability and Speed by ZIP Code Tabulation Area) contains measures of broadband internet access and usage per United States ZIP code tabulation area (ZCTA) in 2014 through 2020. The data is derived primarily from internet service providers' Form 477 reports to the Federal Communications Commission.

Comprehensive dataset containing 1 verified Online Business locations in United States with complete contact information, ratings, reviews, and location data.

Business Software Alliance is a trade association that represents the world's leading software companies, including Autodesk, IBM, and Symantec. The organization's members are committed to promoting the use of legitimate software and ensuring the integrity of their intellectual property.

As a result, the data housed on BSA's website is rich in information related to the software industry, including software licensing, anti-piracy efforts, and digital piracy statistics. The data includes information on software usage, software development, and the impact of piracy on the technology industry. With its focus on promoting legitimate software use, the data on BSA's website provides valuable insights into the global software industry.

This dataset reports on public internet use in the Timberland Regional Library District, a five-county rural library district serving Thurston, Lewis, Mason, Pacific, and Grays Harbor counties. It includes a count of internet sessions and minutes used at each library location.

Dataset

This dataset was created by imagesets8

Contents

This dataset lists various data sources used within the Department of Community Resources & Services for various internal and external reports. This dataset allows individuals and organizations to identify the type of data they are looking for and to which geographical level they are trying to get the data for (i.e. National, State, County, etc.). This dataset will be updated every quarter and should be utilized for research purposes

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. Using advanced web scraping technology, our dataset offers real-time access to job trends, salaries, and skills demand, making it a valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence.

Key Features:

✅232M+ Job Postings Tracked – Data sourced from 92 Million company websites worldwide. ✅7,1M+ Active Job Openings – Updated in real-time to reflect hiring demand. ✅Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

Primary Attributes:

• id (string, UUID) – Unique identifier for the job posting.
• type (string, constant: "job_opening") – Object type.
• title (string) – Job title.
• description (string) – Full job description, extracted from the job listing.
• url (string, URL) – Direct link to the job posting.
• first_seen_at – Timestamp when the job was first detected.
• last_seen_at – Timestamp when the job was last detected.
• last_processed_at – Timestamp when the job data was last processed.

Job Metadata:

• contract_types (array of strings) – Type of employment (e.g., "full time", "part time", "contract").
• categories (array of strings) – Job categories (e.g., "engineering", "marketing").
• seniority (string) – Seniority level of the job (e.g., "manager", "non_manager").
• status (string) – Job status (e.g., "open", "closed").
• language (string) – Language of the job posting.
• location (string) – Full location details as listed in the job description.
• Location Data (location_data) (array of objects)
• city (string, nullable) – City where the job is located.
• state (string, nullable) – State or region of the job location.
• zip_code (string, nullable) – Postal/ZIP code.
• country (string, nullable) – Country where the job is located.
• region (string, nullable) – Broader geographical region.
• continent (string, nullable) – Continent name.
• fuzzy_match (boolean) – Indicates whether the location was inferred.

Salary Data (salary_data)

• salary (string) – Salary range extracted from the job listing.
• salary_low (float, nullable) – Minimum salary in original currency.
• salary_high (float, nullable) – Maximum salary in original currency.
• salary_currency (string, nullable) – Currency of the salary (e.g., "USD", "EUR").
• salary_low_usd (float, nullable) – Converted minimum salary in USD.
• salary_high_usd (float, nullable) – Converted maximum salary in USD.
• salary_time_unit (string, nullable) – Time unit for the salary (e.g., "year", "month", "hour").

Occupational Data (onet_data) (object, nullable)

• code (string, nullable) – ONET occupation code.
• family (string, nullable) – Broad occupational family (e.g., "Computer and Mathematical").
• occupation_name (string, nullable) – Official ONET occupation title.

Additional Attributes:

• tags (array of strings, nullable) – Extracted skills and keywords (e.g., "Python", "JavaScript").

📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

PredictLeads Dataset: https://docs.predictleads.com/v3/guide/job_openings_dataset

Dataset Info:

MyDigitalFootprint (MDF) is a novel large-scale dataset composed of smartphone embedded sensors data, physical proximity information, and Online Social Networks interactions aimed at supporting multimodal context-recognition and social relationships modelling in mobile environments. The dataset includes two months of measurements and information collected from the personal mobile devices of 31 volunteer users by following the in-the-wild data collection approach: the data has been collected in the users' natural environment, without limiting their usual behaviour. Existing public datasets generally consist of a limited set of context data, aimed at optimising specific application domains (human activity recognition is the most common example). On the contrary, the dataset contains a comprehensive set of information describing the user context in the mobile environment.

The complete analysis of the data contained in MDF has been presented in the following publication:

https://www.sciencedirect.com/science/article/abs/pii/S1574119220301383?via%3Dihub

The full anonymised dataset is contained in the folder MDF. Moreover, in order to demonstrate the efficacy of MDF, there are three proof of concept context-aware applications based on different machine learning tasks:

1. A social link prediction algorithm based on physical proximity data,
2. The recognition of daily-life activities based on smartphone-embedded sensors data,
3. A pervasive context-aware recommender system.

For the sake of reproducibility, the data used to evaluate the proof-of-concept applications are contained in the folders link-prediction, context-recognition, and cars, respectively.

This webinar series is for data introductions.. Visit https://dataone.org/datasets/sha256%3Ac3cc2b88b9990c87242567425e238e1c65447f91807c2cc814230250e07920af for complete metadata about this dataset.

Internet Data Center Market Outlook

According to our latest research, the global Internet Data Center market size stood at USD 68.3 billion in 2024, registering a robust growth trajectory. The market is forecasted to reach USD 165.7 billion by 2033, expanding at a healthy CAGR of 10.4% during the 2025-2033 period. The key growth factor driving this surge is the exponential rise in data generation, cloud computing adoption, and the proliferation of digital transformation initiatives across industries worldwide. As organizations increasingly prioritize business continuity, security, and scalability, the demand for advanced data center infrastructure is at an all-time high, shaping the future of the Internet Data Center market.

One of the primary drivers fueling the growth of the Internet Data Center market is the rapid expansion of digital services and applications, which has led to an unprecedented surge in global data traffic. The proliferation of Internet of Things (IoT) devices, video streaming, e-commerce, and social media platforms has necessitated the deployment of high-capacity, low-latency data centers capable of handling massive workloads. Enterprises and service providers are investing heavily in data center modernization, focusing on energy efficiency, automation, and robust connectivity to support these evolving digital ecosystems. The growing emphasis on hybrid and multi-cloud strategies further amplifies the need for flexible and scalable data center solutions, propelling market growth.

Another significant growth factor is the increasing adoption of artificial intelligence (AI), machine learning, and big data analytics across various sectors, including healthcare, finance, and retail. These technologies require substantial computational power and storage capabilities, driving demand for advanced data center infrastructure. Modern data centers are being designed to support high-density computing, GPU acceleration, and edge computing, enabling real-time data processing and analytics at scale. Additionally, the shift toward software-defined data centers (SDDC) and virtualization is transforming traditional data center architectures, enabling greater agility, cost-efficiency, and operational resilience. This evolution is further supported by advancements in network technologies such as 5G, which facilitate faster data transmission and improved user experiences.

Sustainability and energy efficiency have emerged as crucial considerations in the Internet Data Center market, as organizations and governments worldwide prioritize environmental responsibility. Data centers are significant consumers of electricity, prompting the adoption of green technologies, renewable energy sources, and innovative cooling solutions to minimize carbon footprints. Regulatory mandates and industry standards are driving investments in energy-efficient hardware, intelligent power management, and sustainable building practices. Leading market players are increasingly focusing on achieving carbon neutrality and leveraging circular economy principles, which not only reduce operational costs but also enhance brand reputation and stakeholder trust. This sustainable approach is expected to shape investment decisions and technological advancements in the coming years.

As the demand for data processing and storage continues to grow, the concept of a Hyperscale Data Center has emerged as a pivotal solution to meet these needs. Hyperscale data centers are designed to efficiently scale up resources, accommodating the vast amounts of data generated by modern digital activities. These facilities are characterized by their ability to support thousands of servers and millions of virtual machines, ensuring seamless performance and reliability. The architecture of hyperscale data centers focuses on maximizing energy efficiency and optimizing cooling systems, making them a sustainable choice for large-scale operations. As businesses increasingly rely on cloud services and big data analytics, the role of hyperscale data centers becomes ever more critical in providing the necessary infrastructure to support these advanced technologies.

Regionally, the Asia Pacific market is witnessing remarkable growth, outpacing other regions due to rapid digitalization, government initiatives, and increasing internet penetration. Countries such as China, India, and Singapo

This data set contains Production by Planning Area

As of 2019, 31 percent of the Nordic smartphone users believed that companies used their personal data all the time. Those who did not think companies used their data were six percent.

Water Data Online provides free access to nationally consistent, current and historical water data (and related information) that is collected by the Bureau of Meteorology under the Water Regulations (2008). It allows users to view and download standardised data and reports.

Watercourse level and discharge (Water Regulations, Category 1) time series data collected from approximately 3500 measurement stations across Australia is currently available on Water Data Online. This data has generally been supplied by lead water agencies.

The Bureau will continue to work to expand the number of water information categories and water monitoring stations presented. For more information refer to the Water Data Online information sheet

The time period over which data is available varies according to how long the stations have been operating. The period of record for some locations starts in the late 19th century. Water Data Online does not display near real time, or flood data. Water Data Online also contains historical data from some stations that are no longer being operated. Such data can provide valuable insight into environmental changes and help build a more comprehensive national picture of our water resources.