This dataset tracks the updates made on the dataset "API update/Refresh" as a repository for previous versions of the data and metadata.

The Enriched Citation API provides the Intellectual Property 5 (IP5 - EPO, JPO, KIPO, CNIPA, and USPTO) and the Public with greater insight into the patent evaluation process. It allows users to quickly view information about which references, or prior art, were cited in specific patent application Office Actions, including: bibliographic information of the reference, the claims that the prior art was cited against, and the relevant sections that the examiner relied upon. The API allows for daily refresh and retrieval of enrich citation data from Office Actions mailed from October 1, 2017 to 30 days prior to the current date.

MyAnimeList API Download

This is the dataset that I created as part of the Google Data Analytics Professional Certificate capstone project. The MyAnimeList website has a vast repository of ratings and rankings of viewership data that could be used for various methods. I extracted several datasets from the detail API from MyAnimeList (MAL) https://myanimelist.net/apiconfig/references/api/v2 and plan to potentially update data every two weeks.

Many possible uses for this data could be tracking what anime viewers are watching most within a particular time period, what's being scored (out of 10) well and what isn't.

My viz for this data will be part of a tableau dashboard located here. This dashboard allows fans to explore the dataset and locate top scored or popular titles by genre, time period, and demographic (although this field isn't always entered)

Documentation

The extraction and cleaning process is outlined on github here.

Frequency of Updates

I plan on updating this potentially every 2 weeks, this depends on my availability and the interest in this dataset.

Caveats

Extracting and loading this data involved some transformations that should be noted:

• This data only includes titles that correspond with the "tv" ranking category. This was in an effort to streamline extraction and fine tune the analysis. If you would like to see other categories you are welcome to suggest it as an enhancement or use the code create your own dataset. As a result of subsetting on "tv", the dataset excludes the following ranking categories:
  1. All
  2. airing
  3. upcoming
  4. ova
  5. movie
  6. special
  7. bypopularity
  8. favorite
• Adult content - This extract excludes all adult content (r+).
• Note: The previous two points are valid for all tables with the exception of the rank_table. This is the table that was used as a starting point to obtain all MAL ids that were associated with "tv". Because this is a fast download, all categories are included in this table.
• The creation of the alternative_title field in the anime_table. This uses the english version of the name unless it is null, if the value is null, it uses the default name. This was in an effort to make the title accessible to english speakers. The original title field can be used if desired.
• The extraction of the demographic information from the genres field. MyAnimeList includes demographic information (shounen, seinen etc.) in the genres field. I've extracted it so that it could be used as its own field. However, many of those fields are null making it somewhat difficult to use.
• Cleaning processes of data. Various methods of cleaning data have been carried out and are noted on github.
• start_season.year - this field in the anime_table has been modified for null values. If there are null values, the first four characters from the start_date have been used. I will continue to use this method as long as it is viable.

Table Structure

The primary keys in all of the tables (with the exclusion of the tm_ky table) are foreign keys to other tables. As a result, the tables have 2 or more primary keys.

1. anime_demo_table

Field	Type	Primary Key
tm_ky	int	PK
mal_id	int	PK
demo_id	int

1. anime_genres_table

Field	Type	Primary Key
tm_ky	int	PK
mal_id	int	PK
genres_id	int	PK

1. anime_ranking_table

Field	Type	Primary Key
tm_ky	int	PK
mal_id	int	PK
mean	dbl
rank	int
popularity	int
num_scoring_users	int
statistics.watching	int
statistics.completed	int
statistics.on_hold	int
statistics.dropped	int
statistics.plan_to_watch	int
statistics.num_scoring_users	int

1. anime_studios_table

Field	Type	Primary Key
tm_ky	int	PK
mal_id	int	PK
studio_id	int	PK

1. anime_syn_table

Field	Type	Primary Key
tm_ky	int	PK
mal_id	int	PK
synonyms	chr

1. anime_table

Field	Type	Primary Key
tm_ky	int	PK
mal_id	int	PK
title	chr
main_picture.medium	chr
main_picture.large	chr
alternative_titles.en	chr
alternative_titles.ja	chr
start_date	chr
end_date	chr
synopsis	chr
media_type	chr
status	chr
num_episodes	int
start_season.year	int
start_season.season	chr
rating	chr
nsfw	chr
demo_de	chr ...

This dataset shows whether each dataset on data.maryland.gov has been updated recently enough. For example, datasets containing weekly data should be updated at least every 7 days. Datasets containing monthly data should be updated at least every 31 days. This dataset also shows a compendium of metadata from all data.maryland.gov datasets.

This report was created by the Department of Information Technology (DoIT) on August 12 2015. New reports will be uploaded daily (this report is itself included in the report, so that users can see whether new reports are consistently being uploaded each week). Generation of this report uses the Socrata Open Data (API) to retrieve metadata on date of last data update and update frequency. Analysis and formatting of the metadata use Javascript, jQuery, and AJAX.

This report will be used during meetings of the Maryland Open Data Council to curate datasets for maintenance and make sure the Open Data Portal's data stays up to date.

Context

These files contain metadata for more than 700,000 movies listed in the TMDB Dataset. The dataset Update daily to ensure updated movies dataset. Data points include cast, crew, plot keywords, budget, revenue, posters, release dates, languages, production companies, countries, TMDB vote counts and vote averages, reviews, recommendations.

Acknowledgements

This dataset from TMDB Dataset. The Movie Details, Credits and Keywords have been collected from the TMDB Open API. This product uses the TMDB API but is not endorsed or certified by TMDB. Their API also provides access to data on many additional movies, actors and actresses, crew members, and TV shows. You can try it for yourself here.

Some of the things you can do with this dataset:

• Building Content Based and Collaborative Filtering Based Recommendation Engines.
• Predicting movie revenue and/or movie success based on a certain metric.
• What movies tend to get higher vote counts and vote averages on TMDB?

The Forager.ai Global Dataset is a leading source of firmographic data, backed by advanced AI and offering the highest refresh rate in the industry.

| Volume and Stats |

• Over 70M total records, the highest volume in the industry today.
• Every company record refreshed twice a month, offering an unparalleled update frequency.
• Delivery is made every hour, ensuring you have the latest data at your fingertips.
• Each record is the result of an advanced AI-driven process, ensuring high-quality, accurate data.

| Use Cases |

Sales Platforms, ABM and Intent Data Platforms, Identity Platforms, Data Vendors:

Example applications include:

1. Uncover trending technologies or tools gaining popularity.

2. Pinpoint lucrative business prospects by identifying similar solutions utilized by a specific company.

3. Study a company's tech stacks to understand the technical capability and skills available within that company.

B2B Tech Companies:

• Enrich leads that sign-up through the Company Search API (available separately).
• Identify and map every company that fits your core personas and ICP.
• Build audiences to target, using key fields like location, company size, industry, and description.

Venture Capital and Private Equity:

• Discover new investment opportunities using company descriptions and industry-level data.
• Review the growth of private companies and benchmark their strength against competitors.
• Create high-level views of companies competing in popular verticals for investment.

| Delivery Options |

• Flat files via S3 or GCP
• PostgreSQL Shared Database
• PostgreSQL Managed Database
• API
• Other options available upon request, depending on the scale required

Our dataset provides a unique blend of volume, freshness, and detail that is perfect for Sales Platforms, B2B Tech, VCs & PE firms, Marketing Automation, ABM & Intent. It stands as a cornerstone in our broader data offering, ensuring you have the information you need to drive decision-making and growth.

Tags: Company Data, Company Profiles, Employee Data, Firmographic Data, AI-Driven Data, High Refresh Rate, Company Classification, Private Market Intelligence, Workforce Intelligence, Public Companies.

This dataset shows whether each dataset on data.maryland.gov has been updated recently enough. For example, datasets containing weekly data should be updated at least every 7 days. Datasets containing monthly data should be updated at least every 31 days. This dataset also shows a compendium of metadata from all data.maryland.gov datasets.

This report was created by the Department of Information Technology (DoIT) on August 12 2015. New reports will be uploaded daily (this report is itself included in the report, so that users can see whether new reports are consistently being uploaded each week). Generation of this report uses the Socrata Open Data (API) to retrieve metadata on date of last data update and update frequency. Analysis and formatting of the metadata use Javascript, jQuery, and AJAX.

This report will be used during meetings of the Maryland Open Data Council to curate datasets for maintenance and make sure the Open Data Portal's data stays up to date.

This dataset shows whether each dataset on data.maryland.gov has been updated recently enough. For example, datasets containing weekly data should be updated at least every 7 days. Datasets containing monthly data should be updated at least every 31 days. This dataset also shows a compendium of metadata from all data.maryland.gov datasets.

This report was created by the Department of Information Technology (DoIT) on August 12 2015. New reports will be uploaded daily (this report is itself included in the report, so that users can see whether new reports are consistently being uploaded each week). Generation of this report uses the Socrata Open Data (API) to retrieve metadata on date of last data update and update frequency. Analysis and formatting of the metadata use Javascript, jQuery, and AJAX.

This report will be used during meetings of the Maryland Open Data Council to curate datasets for maintenance and make sure the Open Data Portal's data stays up to date.

These datasets contain information on child labor and forced labor worldwide from ILAB’s three flagship reports: Findings on the Worst Forms of Child Labor; List of Goods Produced by Child Labor or Forced Labor; and List of Products Produced by Forced or Indentured Child Labor. There are 14 tables containing data from the 2015-2019 reporting cycles and 11 tables from the 2014 reporting cycle. ILAB plans to update the structure of the API. This information is also available in ILAB’s app, Sweat & Toil: Child Labor, Forced Labor, and Human Trafficking Around the World. For more information, see ILAB’s International Child Labor and Forced Labor Reports page. https://www.dol.gov/agencies/ilab/resources/reports/child-labor/findings https://developer.dol.gov/others/sweat-and-toil/

This dataset shows whether each dataset on data.maryland.gov has been updated recently enough. For example, datasets containing weekly data should be updated at least every 7 days. Datasets containing monthly data should be updated at least every 31 days. This dataset also shows a compendium of metadata from all data.maryland.gov datasets.

This report was created by the Department of Information Technology (DoIT) on August 12 2015. New reports will be uploaded daily (this report is itself included in the report, so that users can see whether new reports are consistently being uploaded each week). Generation of this report uses the Socrata Open Data (API) to retrieve metadata on date of last data update and update frequency. Analysis and formatting of the metadata use Javascript, jQuery, and AJAX.

This report will be used during meetings of the Maryland Open Data Council to curate datasets for maintenance and make sure the Open Data Portal's data stays up to date.

This dataset shows whether each dataset on data.maryland.gov has been updated recently enough. For example, datasets containing weekly data should be updated at least every 7 days. Datasets containing monthly data should be updated at least every 31 days. This dataset also shows a compendium of metadata from all data.maryland.gov datasets.

This report was created by the Department of Information Technology (DoIT) on August 12 2015. New reports will be uploaded daily (this report is itself included in the report, so that users can see whether new reports are consistently being uploaded each week). Generation of this report uses the Socrata Open Data (API) to retrieve metadata on date of last data update and update frequency. Analysis and formatting of the metadata use Javascript, jQuery, and AJAX.

This report will be used during meetings of the Maryland Open Data Council to curate datasets for maintenance and make sure the Open Data Portal's data stays up to date.

This submission defines a DOI for the Great Basin Center for Geothermal Energy's (GBCGE) Subsurface Database Explorer web application and underlying data services, and acknowledges the INGENIOUS project as a major source of funding for data compilation and quality assurance.

The GBCGE Subsurface Database Explorer is an interactive web mapping application that provides public access to the GBCGE Subsurface Database, and its collection of datasets pertinent to geothermal exploration, oil and gas exploration, critical mineral exploration, and other subsurface characterization for the Great Basin Region, western US.

This is a living database, and will be continuously updated with new data and datasets as funding and motivations allow. The underlying database views that populate the web application are on an automated refresh schedule.

Data sources and acknowledgements:

We thank our partners with the Nevada Division of Minerals (NDOM), the Southern Methodist University (SMU), and Great Basin State Geological Surveys for their active efforts in data curation, schema design, and quality assurance. We also thank contributors among the USGS, Oregon Institute of Technology, State Divisions of Water Resources, State Divisions of Oil, Gas, and Minerals, and State Geological Surveys for open data availability and direct contributions made under the National Geothermal Data System (NGDS).

Contains detailed information derived from the Office actions issued by patent examiners to applicants during the patent examination process. The Office Action is a written notification to the applicant of the examiners decision on patentability. It generally discloses the reasons for any rejections, objections, or requirements and includes relevant information or references that the applicant may find useful for responding to the examiner and deciding whether to continue prosecuting the application. This API allows for daily refresh and retrieval of citation data from Office Actions mailed from June 1, 2018 to 180 days prior to the current date. It uses information derived from citations referenced on the Form PTO-892, Form PTO-1449, and text of Office actions. Due to popular requests/demands, we have updated OA Citations API. Please see the JSON field mappings between OA Citations v1 and v2 as some fields have been updated in v2.

Refresh X Inc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Contains detailed information derived from the Office actions issued by patent examiners to applicants during the patent examination process. The Office Action is a written notification to the applicant of the examiners decision on patentability. It generally discloses the reasons for any rejections, objections, or requirements and includes relevant information or references that the applicant may find useful for responding to the examiner and deciding whether to continue prosecuting the application. This API allows for daily refresh and retrieval of rejection data from Office Actions mailed from June 1, 2018 to 180 days prior to the current date. It contains document level data including the type of actions taken on claims in the office action. Due to popular requests/demands, we have updated the OA Rejections API. Please see the JSON field mappings between OA Rejections v1 and v2 as some fields have been updated in v2.

Power your US Operations, HR Tech, and Market Intelligence engines with the most comprehensive database of the American workforce. This dataset offers a structured, historical view of 194,408,909 US professionals, capturing career trajectories, educational backgrounds, and skill sets across every state and industry.

With coverage nearing 100% of the active US white-collar workforce, our US Professional Identity Graph provides a dynamic view of talent. We map the relationships between People, Companies, Skills, and Schools, allowing you to answer complex questions about domestic talent migration, skill supply, and organizational hierarchies.

Key Use Cases 1. B2B Data Enrichment & CRM Hygiene Turn a simple email address or name into a full 360-degree US prospect profile.

Append: Add currentCompanies, jobTitle, and industry to your existing US leads.

Lead Scoring: Use connectionsCount and recommendations as proxies for influence within US markets.

Refresh: Identify when a prospect has changed jobs (lastUpdated) to trigger "New Role" outreach campaigns.

1. Talent Intelligence & Recruitment Build the next generation of US hiring tools.

Sourcing: Query by complex skill combinations (e.g., "Python" + "TensorFlow" + "5 Years Experience" in "San Francisco").

Alumni Targeting: Use educations data to find candidates from specific US Universities (Ivy League, State Colleges, etc.).

DEI Analytics: Leverage pronoun and volunteerExperiences data for diversity and inclusion benchmarking.

1. US Labor Market Analysis

Migration Trends: Track talent movement between states (e.g., "Tech talent moving from CA to TX").

Skill Trends: Analyze the rise of specific skills across US industries.

Data Dictionary & Schema Attributes Our schema is normalized for easy ingestion. We provide over 30 rich attributes per profile, grouped into five core intelligence clusters:

1. Identity & Social (The "Who")

publicId / vanity: The unique handle for the profile (e.g., /in/john-doe).

urn: The immutable, system-unique identifier.

fullName, firstName, lastName: Parsed name fields.

headline & summary: The professional's self-described bio and taglines.

pronoun: Self-identified pronouns.

logoUrl: Profile image link.

openToWork: Indicator of active job-seeking status.

1. Professional Graph (The "What")

currentCompanies: Detailed object containing Company Name, Title, Start Date.

previousCompanies: Historical array of past roles, creating a full resume view.

industry: Standardized industry classification.

1. Capability & Skills (The "How")

skills: Array of endorsed skills (e.g., "Project Management", "SQL").

languages: Spoken languages and proficiency levels.

certifications: Professional licenses and validity dates.

courses & honors: Academic and professional awards.

educations: Full academic history including Degree, School, and Dates.

1. Influence & Content (The "Reach")

connectionsCount: Total network size.

followersCount: Measure of audience reach.

recommendations: Text of received professional endorsements.

organizations: Memberships in professional bodies or non-profits.

patents, projects, publications: Intellectual property and portfolio items.

1. Location & Metadata

locationName: City/Metro area (e.g., "Greater New York City Area", "Austin, Texas").

locationCountry: Fixed to "US".

lastUpdated: Timestamp of the most recent data refresh.

id: 194408909 - Fill Rate: 100% fullName: 194392269 - Fill Rate: 99.99% firstName: 194391083 - Fill Rate: 99.99% lastName: 193031965 - Fill Rate: 99.29% publicId: 194408909 - Fill Rate: 100% urn: 194408909 - Fill Rate: 100% headline: 194260405 - Fill Rate: 99.92% summary: 41525593 - Fill Rate: 21.36% industry: 143067057 - Fill Rate: 73.59% locationName: 194408824 - Fill Rate: 100% locationCountry: 194408909 - Fill Rate: 100% logoUrl: 62644925 - Fill Rate: 32.22% connectionsCount: 139069652 - Fill Rate: 71.53% followersCount: 140881048 - Fill Rate: 72.47% currentCompanies: 133983286 - Fill Rate: 68.92% previousCompanies: 67758867 - Fill Rate: 34.85% educations: 88604497 - Fill Rate: 45.58% volunteerExperiences: 12375279 - Fill Rate: 6.37% skills: 75429843 - Fill Rate: 38.8% pronoun: 14806274 - Fill Rate: 7.62% related: 141341109 - Fill Rate: 72.7% languages: 14267971 - Fill Rate: 7.34% recommendations: 10304568 - Fill Rate: 5.3% certifications: 19279558 - Fill Rate: 9.92% courses: 5153692 - Fill Rate: 2.65% honors: 7139463 - Fill Rate: 3.67% organizations: 6840143 - Fill Rate: 3.52% patents: 411407 - Fill Rate: 0.21% projects: 4099324 - Fill Rate: 2.11% publications: 2927800 - Fill Rate: 1.51% lastUpdated: 194408909 - Fill Rate: 100% member_id: 193803832 - Fill Rate: 99.69% company_id: 85095974 - Fill Rate: 43.77% num_recommenders: 10304568 - Fill Rate: 5.3% experiences_count: 146291011 - Fill Rate: 75.25% educations_count: 88604834 - Fill Rate: 45.58% linkedin_name: 194408909 - Fill Rate: 100% endorsers: 6508123 - Fill Rate: 3.35% open_to_work: 6433122 - Fill Rate: 3.3...

Scale your HR Tech, B2B Sales, and Market Intelligence engines with the world’s most comprehensive database of public professional profiles. This dataset offers a structured, historical view of the global workforce, capturing the career trajectories, educational backgrounds, and skill sets of over 830,042,175 professionals across 190+ countries.

Unlike static contact lists, our Professional Identity Graph provides a dynamic view of an individual's career. We map the relationships between People, Companies, Skills, and Schools, allowing you to answer complex questions about talent migration, skill supply, and organizational hierarchies.

All profiles are matched to a public Linkedin URL

Key Use Cases 1. B2B Data Enrichment & CRM Hygiene Turn a simple email address or name into a full 360-degree prospect profile.

Append: Add currentCompanies, jobTitle, and industry to your existing leads.

Lead Scoring: Use connectionsCount and recommendations as proxies for influence and decision-making power.

Refresh: Identify when a prospect has changed jobs (lastUpdated) to trigger "New Role" outreach campaigns.

1. Talent Intelligence & Recruitment Build the next generation of hiring tools.

Sourcing: Query by complex skill combinations (e.g., "Python" + "TensorFlow" + "5 Years Experience").

Alumni Targeting: Use educations data to find candidates from target universities.

DEI Analytics: Leverage pronoun and volunteerExperiences data for diversity and inclusion benchmarking.

1. Investment & Labor Market Analysis

Headcount Growth: Track currentCompanies vs. previousCompanies to measure company growth or attrition rates in real-time.

Skill Trends: Analyze the rise of specific skills (e.g., "Generative AI") across specific industries or regions.

Data Dictionary & Schema Attributes Our schema is normalized for easy ingestion. We provide over 30 rich attributes per profile, grouped into five core intelligence clusters:

1. Identity & Social (The "Who")

publicId / vanity: The unique handle for the profile (e.g., /in/john-doe).

urn: The immutable, system-unique identifier.

fullName, firstName, lastName: Parsed name fields.

headline & summary: The professional's self-described bio and taglines.

pronoun: Self-identified pronouns (he/him, she/her, etc.).

logoUrl: Profile image link.

openToWork: Indicator of active job-seeking status.

1. Professional Graph (The "What")

currentCompanies: Detailed object containing Company Name, Title, Start Date.

previousCompanies: Historical array of past roles, creating a full resume view.

industry: Standardized industry classification.

1. Capability & Skills (The "How")

skills: Array of endorsed skills (e.g., "Project Management", "SQL").

languages: Spoken languages and proficiency levels.

certifications: Professional licenses and validity dates.

courses & honors: Academic and professional awards.

educations: Full academic history including Degree, School, and Dates.

1. Influence & Content (The "Reach")

connectionsCount: Total network size.

followersCount: Measure of audience reach.

recommendations: Text of received professional endorsements.

organizations: Memberships in professional bodies or non-profits.

patents, projects, publications: Intellectual property and portfolio items.

1. Location & Metadata

locationName: City/Metro area (e.g., "Greater New York City Area").

locationCountry: ISO-2 Country Code.

lastUpdated: Timestamp of the most recent data refresh.

fullName: 830042175 - Fill Rate: 99.99% firstName: 830023323 - Fill Rate: 99.98% lastName: 822995392 - Fill Rate: 99.14% publicId / vanity: 830159658 - Fill Rate: 100% urn: 830159658 - Fill Rate: 100% headline: 829660649 - Fill Rate: 99.94% summary: 154826408 - Fill Rate: 18.65% industry: 569584072 - Fill Rate: 68.61% locationName: 829491476 - Fill Rate: 99.92% locationCountry: 830159658 - Fill Rate: 100% logoUrl: 225683142 - Fill Rate: 27.19% connectionsCount: 563236676 - Fill Rate: 67.85% followersCount: 569950689 - Fill Rate: 68.66% currentCompanies: 544595655 - Fill Rate: 65.6% previousCompanies: 244822218 - Fill Rate: 29.49% educations: 378348844 - Fill Rate: 45.58% volunteerExperiences: 33804455 - Fill Rate: 4.07% skills: 296336188 - Fill Rate: 35.7% pronoun: 38741090 - Fill Rate: 4.67% related: 576329691 - Fill Rate: 69.42% languages: 73444194 - Fill Rate: 8.85% recommendations: 27940603 - Fill Rate: 3.37% certifications: 65446443 - Fill Rate: 7.88% courses: 21095553 - Fill Rate: 2.54% honors: 17348831 - Fill Rate: 2.09% organizations: 14691528 - Fill Rate: 1.77% patents: 1012239 - Fill Rate: 0.12% projects: 16879774 - Fill Rate: 2.03% publications: 9748127 - Fill Rate: 1.17% lastUpdated: 830159658 - Fill Rate: 100% openToWork: 42157137 - Fill Rate: 5.08%

Compliance & Data Governance We understand that compliance is paramount when handling professional data.

Source: All data is aggregated strictly from Public Web Sources. We do not hack, credential-stuff, or access data behind login walls....

The Application Programming Interface (API) is a programming interface that allows access to data and methods of the German Digital Library (DDB). It allows the development of diverse applications that use the contents contained in the DDB and display them according to their own wishes and embed them in different contexts. The API is open to all people.

To use the API of the DDB, authentication in the form of a key (API Key) is required. (Information on requesting an API access). Only CC0-licensed metadata is output via the API of the DDB.

The selection of the ZIP archives listed here is limited to holdings of Berlin institutions and represents a supplementary offer from digiS. The XML files collected in the archives are in EDM format and have been downloaded via the API of the DDB.

The digitisations referenced in the metadata are subject to open licenses. The respective institution may offer further data sets or digitalisations, but not necessarily under open licenses.

It is intended to update the data sets every six months.

** Last update of the ZIP archives: 2022-05-30**

This dataset contains battery-related data collected from the Materials Project database. It focuses on insertion-type battery materials and provides detailed information on chemical composition, framework structure, electrochemical parameters, and voltage characteristics. Each entry represents a unique material with computed or simulated parameters relevant to battery performance and stability.

The dataset was last updated on October 16, 2025, ensuring that it includes the most recent computational results available in the Materials Project database.

Note: The column thermo_type has 100% missing values (null) and may be safely ignored for data analysis or modeling.

📊 Main Columns Overview:

• battery_type: Type of battery (e.g., insertion)
• battery_id: Unique identifier for each material entry
• thermo_type: Thermodynamic classification (currently null)
• battery_formula: Chemical formula of the battery material
• working_ion: Type of working ion (e.g., Li, Na, Mg)
• max_voltage: Maximum computed voltage
• formula_charge, formula_discharge: Chemical formulas before and after charge/discharge
• capacity_grav, capacity_vol: Gravimetric and volumetric capacity
• elements, chemsys: Elemental composition and chemical system
• last_updated: Date of last data update etc.

🔬 Potential Applications: - Screening of novel battery materials - Machine learning and data-driven material discovery - Analysis of structure-property-performance relationships - Development of predictive electrochemical models

The data set is refreshed on a daily basis by 1:45 PM. The website will reflect the last time the data set was updated and the total count of rows. The grid on the “Data” tab will display the up to date data. However, in certain situations there is a delay in the refresh of the downloadable data file. Sometimes the downloadable file does not reflect the updates to the data in the portal. After a delay (duration has been variable; up to 30 minutes), the file will be updated on the server and then downloads will include the updated data.