Between June and December 2024, almost 38 percent of user reports were for reasons of abuse and harassment on X (formerly Twitter), while 31 percent were reports of hateful content. Additionally, over 13 percent of user reports were due to violent content.

Between July and December 2024, X (formerly Twitter) received over 69 million user reports of abuse and harassment on the platform and over 56 million reports of hateful conduct. Additionally, during this six-month period, there were over 23 million reports of violent content. Company financials have not been available since X/Twitter’s last annual report was published in early 2022.

High Frequency Indicator: The dataset contains year- and month-wise compiled data from the year 2021 to till date on number general queries received by X (Twitter) about its user accounts.

The data compiled is based on the monthly transparency reports published by Koo in accordance with Rule 4(1)(d) of the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 (IT Rules, 2021)

High Frequency Indicator: This dataset compiles year- and month-wise data from 2021 to the present on the number and distribution of user grievances received by X (formerly Twitter), along with the actions taken by the platform. The data is based on the monthly transparency reports published under Rule 4(1)(d) of the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021.

From June 2021 to August 2025, X reported grievance data in absolute numbers across various categories such as illegal activities, IP-related infringements, Abuse/Harassment, Child Sexual Exploitation, Defamation, Hateful Conduct, Impersonation, Misinformation, etc. During this period, the dataset reflects these absolute values directly.

Beginning September 2025, X discontinued reporting grievance counts by category in absolute numbers and instead published percentage share distributions of grievances and URLs actioned. The transparency reports continued to provide the total number of grievances received and total URLs actioned, from which the dataset estimates the absolute category-wise values by applying the reported percentage shares. Additionally, for comparability across the entire time series, percentage shares for months prior to September 2025 have been computed based on the reported absolute values.

Quarterly reported position values for GLOBAL X FDS held by Envision Financial Transparency LLC

General description

This data file is a compilation of national greenhouse gas emissions (GHG) inventories, sourced from the Common Reporting Tables (CRTs) that countries submit to the UNFCCC. The CRTs themselves require significant manipulation before one can begin any data analysis. The objective of this dataset is therefore to put the national inventories into a tidy, consistent format that better suits user needs. I sourced the original CRT files from the UNFCCC (e.g. https://unfccc.int/ghg-inventories-annex-i-parties/2024; https://unfccc.int/first-biennial-transparency-reports) and reformatted their summary reports into a single tidy, structured data table.

Data structure

The national GHG inventories provide emissions estimates along four dimensions: countries x years x sectors x gases.

• Countries: currently only Annex I countries consistently submit CRTs each year. Non-Annex I countries submit at irregular intervals, although this may change. Recently there have also been significant delays in submissions as as countries move to the new reporting format. The full list of countries covered and associated files is in the "countries" tab of the spreadsheet.
• Years: reporting starts in 1990 and runs until two years prior to the publication of each inventory (e.g. inventories submitted in 2024 report up to 2022).
• Sectors: emissions are split into sectors as set out by the Intergovernmental Panel on Climate Change (IPCC) Task Force on National Greenhouse Gas Inventories (TFI). The six main sectors are (1) Energy, (2) Industrial processes and product use, (3) Agriculture, (4) Land use, land-use change and forestry, (5) Waste, and (6) Other. A general description of the sectors is available in the TFI guidance. In this data file the five high level sectors are split into 41 individual categories, which is the most detailed level of reporting provided in the CRT summary sheets. I have included only "leaf nodes" from the sector hierarchy in the file, which are sectors that have no further child sectors. This means that you can safely sum up all sectors for each country without double counting. The higher level sector categories are provided as variables for convenient aggregation. The "sector" tab of the spreadsheet shows which sectors are included and how they fit into the TFI hierarchy.
• Gases: countries report emissions from CO2, CH4, N2O and F-gases (HFCs, PFCs, NF3, SF6). I have converted emissions from the different gases to CO2 equivalents using global warming potentials with a 100 year time horizon from the IPCC 5th Assessment Report (GWP100 AR5). More recent GWP100 values have been published in the IPCC AR6, but I have not yet figured out if it is possible to extract F-gases from every CRT in original units, which would be necessary to update the values. CH4 and N2O can be reconverted into their original units by dividing by 28 and 265, respectively. A full list of global warming potentials can be found here.

What can I use this data for?

The national GHG inventories are a formal part of the Paris Agreement and are intended to facilitate the tracking of progress towards national and global climate objectives. As such this data can be used to evaluate whether or not countries are progressing towards their stated goals (e.g. the NDCs and long-term net zero targets). It can also be used for a wide range of other applications, such as to track the effectiveness and outcomes of different policy interventions.

The dataset is currently not complete for all countries. It is therefore not suited to tracking total global GHG emissions. For that purpose, I recommend to use one of the other 3rd party datasets such as EDGAR, PRIMAP, CEDS, or the Global Carbon Budget for CO2 emissions.

Note that there is also an ongoing debate on the differences between national inventory reporting of LULUCF emissions and removals versus estimates from global bookkeeping models. This has significant implications for national and global net zero targets. It is important to be aware of those nuances when using national inventory LULUCF data.

Issues, updates, changes

• Country coverage: I will semi-regularly check the UNFCCC submissions and include new CRTs. Once all Annex I countries have submitted theirs, I will release a first "full version" (v1).
• Validation: I have cross-validated the data by (1) calculating whether leaf node sectors sum to reported emissions at higher levels in the hiararchy; and (2) by calculating whether leaf node sectors sum to total reported GHG emissions from Table10s1 in the CRTs. So far I have observed differences with respect to #2 on the order of 0.01-0.8% of total national ghg emissions for a handful of countries (Spain, Latvia, Poland, Portugal) in certain years. There is a larger difference in Khazakstan (1.95%) in a single year (2020). For both cases I don't yet know why. Any help with validation is highly appreciated.
• Activity data: The CRTs contain underlying data on activities, fuel use and emissions factors. These are especially useful for tracking changes and attributing the impacts of policies. I may start to include these in future versions as a separate sheet.

Code availability

The Github repository is here: https://github.com/lambwf/Tidy-GHG-Inventories/

And the main script for extracting CRTs is here: https://github.com/lambwf/Tidy-GHG-Inventories/blob/main/read_crts.Rmd

Transparent polycrystalline ceramics present significant economical and functional advantages over single crystal materials for optical, communication, and laser technologies. To date, transparency in these ceramics is ensured either by an optical isotropy (i.e., cubic symmetry) or a nanometric crystallite size, and the main challenge remains to eliminate porosity through complex high pressure–high temperature synthesis. Here we introduce a new concept to achieve ultimate transparency reaching the theoretical limit. We use a controlled degree of chemical disorder in the structure to obtain optical isotropy at the micrometer length scale. This approach can be applied in the case of anisotropic structures and micrometer scale crystal size ceramics. We thus report Sr1+x/2Al2+xSi2–xO8 (0 x ≤ 0.4) readily scalable polycrystalline ceramics elaborated by full and congruent crystallization from glass. These materials reach 90% transmittance. This innovative method should drive the development of new highly transparent materials with technologically relevant applications.

The environmental and social impact of fashion companies is increasingly under scrutiny. However, not all companies make the relevant information and metrics available in order to effectively ascertain their sustainability. In a 2025 list compiled to rank the most transparent fashion companies in the world, H&M came out as the highest scoring fashion brand in terms of transparency. The apparel retailer received a transparency index score of 71 percent. This was eight percent higher than Tenzenis, with 63 percent. Apparel manufacturing One of the key pieces of information to evaluate a company’s environmental and social impact is the number of and location of their supplier factories. A common theme among fast fashion companies is that China is the country with the highest number of supplier factories. Turkey also ranked in the top three supplier countries for all of Inditex, H&M, and ASOS, three of the largest European fast fashion retailers. In financial year 2024, the Inditex Group nearly four thousand factories in Asia. Companies’ attempts to be seen as more sustainable Such rankings can be useful for consumers to assess how sustainable a company is, something which is increasingly important to consumers. However, in a 2022 survey, fashion executives named a number of challenges when it comes to improving perceptions of their company’s sustainability credentials. At the top of the list was a lack of standards to assess sustainability performance. Almost four-fifths of respondents cited this as a challenge. 28 percent of fashion executives said that the reduction of environmental impact linked with supply chains was a challenge.

💰 File A: Account Balances – U.S. Federal Budget Execution (DATA Act)

• Maintainer: Terry Eppler
• Source: U.S. Department of the Treasury | Office of Management and Budget (OMB)
• Standard: DATA Act (Pub. L. 113–101) | DAIMS v2.1+ | OMB Circular A-11 Section 130
• Availability: The dataset and source files also available here Hugging Face

🧾 Overview

This dataset contains U.S. federal agency Account Balances (File A) submitted as part of the Digital Accountability and Transparency Act of 2014 (DATA Act). Each row reflects budgetary account balances at the Treasury Appropriation Fund Symbol (TAFS) level.

This is the core financial summary used for transparency, cross-agency reporting, and public display on USAspending.gov.

🎯 Purpose

The File A dataset:

• 📋 Tracks congressional budget authority by account
• 🏛️ Enables public transparency into how the government allocates and spends funds
• 🧮 Supports reconciliation with File B (Program Activity/Object Class) and File C (Awards)
• 📊 Powers dashboards, analytics, and federal budget audits
• 🧠 Provides structured training data for ML/NLP in public finance

🧱 Data Schema

Each row reflects one TAS at a specific fiscal period.

🔄 File Relationships

File A is the first in a family of interrelated DATA Act files:

📈 Sample Use Case

from datasets import load_dataset

ds = load_dataset("your-org/file-a-account-balances")

# Filter by agency and fiscal year
energy_2022 = ds.filter(lambda x: x["agency_identifier"] == "089" and x["fiscal_year"] == 2022)

# Summarize obligations
total_obligated = sum(float(x["obligations_incurred_total_by_tas"]) for x in energy_2022)
print(f"Total Obligated for DOE (2022): ${total_obligated:,.2f}")

Monatlich aktive Nutzer der größten Social-Media-Plattformen in Deutschland basierend auf DSA Article 24(2) Transparency Reports.

During the second half of 2024, X (formerly Twitter) received 97,006 removal requests from government entities. In October 2022, Elon Musk acquired Twitter for 44 billion US dollars. As a result of the deal, the company was taken off the stock market and no transparency reports were available in 2022 and 2023.

Fashion retailer Zara scored its highest rating in the decarbonization area, with a score of 48. According to a recent transparency index covering major retail companies, the overall performance of Zara earned the company a score of 35 out of 150 in 2025.