Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly

About

Data from the State of California. From website:

Access raw State data files, databases, geographic data, and other data sources. Raw State data files can be reused by citizens and organizations for their own web applications and mashups.

Openness

Open. Effectively in the public domain. Terms of use page says:

In general, information presented on this web site, unless otherwise indicated, is considered in the public domain. It may be distributed or copied as permitted by law. However, the State does make use of copyrighted data (e.g., photographs) which may require additional permissions prior to your use. In order to use any information on this web site not owned or created by the State, you must seek permission directly from the owning (or holding) sources. The State shall have the unlimited right to use for any purpose, free of any charge, all information submitted via this site except those submissions made under separate legal contract. The State shall be free to use, for any purpose, any ideas, concepts, or techniques contained in information provided through this site.

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

China Internet Service: Number of Website data was reported at 4.460 Unit mn in Dec 2024. This records an increase from the previous number of 3.910 Unit mn for Jun 2024. China Internet Service: Number of Website data is updated semiannually, averaging 2.939 Unit mn from Dec 2000 (Median) to Dec 2024, with 49 observations. The data reached an all-time high of 5.440 Unit mn in Jun 2018 and a record low of 0.243 Unit mn in Jun 2001. China Internet Service: Number of Website data remains active status in CEIC and is reported by China Internet Network Information Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICE: Internet: Number of Domain and Website.

1. Website usage instructions2. Website usage case instructions3. Website unit introduction

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.

dbRES is a web-oriented comprehensive database for RNA Editing Site. dbRES contain only experimental validated RNA Editing Site. All the data in dbRES was manually collected from literatures reporting related experiment result or the GeneBank database. dbRES now contains all together 5437 RNA edit site data. dbRES covers altogether 95 organisms from 251 transcripts. RNA editing is a post-transcriptional modification of RNA and markedly increases the complexity of the transcriptome. RNA editing occurs in the nucleus, as well as in mitochondria and plastids. To date such changes have been observed in prokaryotes, plants, animals and virus. The diversity of this widespread phenomenon includes nucleoside modifications, nucleotide additions and insertions, either in coding or non-coding sequences of RNA, which can occur concomitantly with transcription and splicing processes.

Web Site Source, Inc. Whois Database, discover comprehensive ownership details, registration dates, and more for Web Site Source, Inc. with Whois Data Center.

This data about nola.gov provides a window into how people are interacting with the the City of New Orleans online. The data comes from a unified Google Analytics account for New Orleans. We do not track individuals and we anonymize the IP addresses of all visitors.

This collaborative interdisciplinary research project aims to consolidate, into a single user-friendly database, information about volcanic products detected in Antarctica. By consolidating information about volcanic sources, and physical and geochemical characteristics of volcanic products, this systematic data collection approach will improve the ability of researchers to identify volcanic ash, or tephra, from specific volcanic eruptions that may be spread over large areas in a geologically instantaneous amount of time. AntT database is designed to assist in the identification and cross-correlation of time intervals in various paleoclimate archives that contain volcanic layers from often unknown sources.

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

Performance comparison of vulnerability mining software.

This group includes the provision of infrastructure for hosting, data processing services and related activities, as well as search facilities and other portals for the Internet.

Estrategias WebSite S.L. Whois Database, discover comprehensive ownership details, registration dates, and more for Estrategias WebSite S.L. with Whois Data Center.

This group includes the provision of infrastructure for hosting, data processing services and related activities, as well as search facilities and other portals for the Internet.

This Website Statistics dataset has four resources showing usage of the Lincolnshire Open Data website. Web analytics terms used in each resource are defined in their accompanying Metadata file.

• Website Usage Statistics: This document shows a statistical summary of usage of the Lincolnshire Open Data site for the latest calendar year.

• Website Statistics Summary: This dataset shows a website statistics summary for the Lincolnshire Open Data site for the latest calendar year.

• Webpage Statistics: This dataset shows statistics for individual Webpages on the Lincolnshire Open Data site by calendar year.

• Dataset Statistics: This dataset shows cumulative totals for Datasets on the Lincolnshire Open Data site that have also been published on the national Open Data site Data.Gov.UK - see the Source link.

  Note: Website and Webpage statistics (the first three resources above) show only UK users, and exclude API calls (automated requests for datasets). The Dataset Statistics are confined to users with javascript enabled, which excludes web crawlers and API calls.

These Website Statistics resources are updated annually in January by the Lincolnshire County Council Business Intelligence team. For any enquiries about the information contact opendata@lincolnshire.gov.uk.

The NIST Chemistry WebBook provides users with easy access to chemical and physical property data for chemical species through the internet. The data provided in the site are from collections maintained by the NIST Standard Reference Data Program and outside contributors. Data in the WebBook system are organized by chemical species. The WebBook system allows users to search for chemical species by various means. Once the desired species has been identified, the system will display data for the species. Data include thermochemical properties of species and reactions, thermophysical properties of species, and optical, electronic and mass spectra.

University e-mail number and injectable URL statistics.

The Lahman Baseball Database is a comprehensive, open-source compilation of statistics and player data for Major League Baseball (MLB). It contains relational data from the 19th century through the most recent complete season, including batting, pitching, and fielding statistics, player demographics, awards, team performance, and managerial records.

This dataset is widely used for exploratory data analysis, statistical modeling, predictive analysis, machine learning, and sports performance forecasting.

This dataset is the latest CSV release of the Lahman Baseball Database, downloaded directly from https://sabr.org/lahman-database/. It includes historical MLB data spanning from 1871 to 2024, organized across 27 structured tables such as: - Batting: Player-level batting stats per year - Pitching: Season-level metrics - People: Biographical data (birth/death, handedness, debut/finalGame) - Teams, Managers: Team records - BattingPost, PitchingPost, FieldingPost: Post-season stats - AllstarFull: all star game - statsHallOfFame: Historical awards and recognitions

Items to explore: - Track league-wide trends in home runs, strikeouts, or batting averages over time - Compare player performance by era, position, or righty/lefty - Create a timeline showing changes in a teams win-loss records - Map birthplace distributions of MLB players over time - Estimate the impact of rule changes on player stats (pitch clock, DH) - Model factors that influence MVP or Cy Young award wins - Predict a players future performance based on historical stats

📘 License

This dataset is released under the Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) license. Attribution is required. Derivative works must be shared under the same license.

📝 Official source: https://sabr.org/lahman-database/ 📥 Direct data page: https://www.seanlahman.com/baseball-archive/statistics/ 🖊️ R-Package Documentation: https://cran.r-project.org/web/packages/Lahman/Lahman.pdf

0.1 Copyright Notice & Limited Use License This database is copyright 1996-2025 by SABR, via generious donation from Sean Lahman. This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. For details see: http://creativecommons.org/licenses/by-sa/3.0/ For licensing information or further information, contact Scott Bush at: sbush@sabr.org 0.2 Contact Information Web site: https://sabr.org/lahman-database/ E-Mail: jpomrenke@sabr.org

This group includes the provision of infrastructure for hosting, data processing services and related activities, as well as search facilities and other portals for the Internet.