Inflation Rate in the United States increased to 3 percent in September from 2.90 percent in August of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Expectations in the United States decreased to 3.20 percent in October from 3.40 percent in September of 2025. This dataset provides - United States Consumer Inflation Expectations- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

The Federal Reserve sets interest rates to promote conditions that achieve the mandate set by the Congress — high employment, low and stable inflation, sustainable economic growth, and moderate long-term interest rates. Interest rates set by the Fed directly influence the cost of borrowing money. Lower interest rates encourage more people to obtain a mortgage for a new home or to borrow money for an automobile or for home improvement. Lower rates encourage businesses to borrow funds to invest in expansion such as purchasing new equipment, updating plants, or hiring more workers. Higher interest rates restrain such borrowing by consumers and businesses.

Content

This dataset includes data on the economic conditions in the United States on a monthly basis since 1954. The federal funds rate is the interest rate at which depository institutions trade federal funds (balances held at Federal Reserve Banks) with each other overnight. The rate that the borrowing institution pays to the lending institution is determined between the two banks; the weighted average rate for all of these types of negotiations is called the effective federal funds rate. The effective federal funds rate is determined by the market but is influenced by the Federal Reserve through open market operations to reach the federal funds rate target. The Federal Open Market Committee (FOMC) meets eight times a year to determine the federal funds target rate; the target rate transitioned to a target range with an upper and lower limit in December 2008. The real gross domestic product is calculated as the seasonally adjusted quarterly rate of change in the gross domestic product based on chained 2009 dollars. The unemployment rate represents the number of unemployed as a seasonally adjusted percentage of the labor force. The inflation rate reflects the monthly change in the Consumer Price Index of products excluding food and energy.

Acknowledgements

The interest rate data was published by the Federal Reserve Bank of St. Louis' economic data portal. The gross domestic product data was provided by the US Bureau of Economic Analysis; the unemployment and consumer price index data was provided by the US Bureau of Labor Statistics.

Inspiration

How does economic growth, unemployment, and inflation impact the Federal Reserve's interest rates decisions? How has the interest rate policy changed over time? Can you predict the Federal Reserve's next decision? Will the target range set in March 2017 be increased, decreased, or remain the same?

Cost of food in the United States increased 3.10 percent in September of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Core consumer prices in the United States increased 3 percent in September of 2025 over the same month in the previous year. This dataset provides - United States Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms Forex Live and FXstreet over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.

To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.

We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment.

Examples of Annotated Headlines


    Forex Pair
    Headline
    Sentiment
    Explanation




    GBPUSD 
    Diminishing bets for a move to 12400 
    Neutral
    Lack of strong sentiment in either direction


    GBPUSD 
    No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft 
    Positive
    Positive sentiment towards GBPUSD (Cable) in the near term


    GBPUSD 
    When are the UK jobs and how could they affect GBPUSD 
    Neutral
    Poses a question and does not express a clear sentiment


    JPYUSD
    Appropriate to continue monetary easing to achieve 2% inflation target with wage growth 
    Positive
    Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply


    USDJPY
    Dollar rebounds despite US data. Yen gains amid lower yields 
    Neutral
    Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other


    USDJPY
    USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains 
    Negative
    USDJPY is expected to reach a lower value, with the USD losing value against the JPY


    AUDUSD

RBA Governor Lowe’s Testimony High inflation is damaging and corrosive

    Positive
    Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD.

Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the FinBERT model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.

Core consumer prices in Egypt increased 12.10 percent in October of 2025 over the same month in the previous year. This dataset provides - Egypt Core Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Japan increased to 3 percent in October from 2.90 percent in September of 2025. This dataset provides the latest reported value for - Japan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing.

Supplement to Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness (https://philpapers.org/rec/PEROAL-2).

Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness move from the features of the ERP characterized in Occipital and Left Temporal EEG Correlates of Phenomenal Consciousness (Pereira, 2015, https://doi.org/10.1016/b978-0-12-802508-6.00018-1, https://philpapers.org/rec/PEROAL) towards the instantaneous amplitude and frequency of event-related changes correlated with a contrast in access and in phenomenology.

Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness proceed as following.

In the first section, empirical mode decomposition (EMD) with post processing (Xie, G., Guo, Y., Tong, S., and Ma, L., 2014. Calculate excess mortality during heatwaves using Hilbert-Huang transform algorithm. BMC medical research methodology, 14, 35) Ensemble Empirical Mode Decomposition (postEEMD) and Hilbert-Huang Transform (HHT).

In the second section, calculated the variance inflation factor (VIF).

In the third section, partial least squares regression (PLSR): the minimal root mean squared error of prediction (RMSEP).

In the last section, partial least squares regression (PLSR): significance multivariate correlation (sMC) statistic.

Predict students' dropout and academic success

Investigating the Impact of Social and Economic Factors

By [source]

About this dataset

This dataset provides a comprehensive view of students enrolled in various undergraduate degrees offered at a higher education institution. It includes demographic data, social-economic factors and academic performance information that can be used to analyze the possible predictors of student dropout and academic success. This dataset contains multiple disjoint databases consisting of relevant information available at the time of enrollment, such as application mode, marital status, course chosen and more. Additionally, this data can be used to estimate overall student performance at the end of each semester by assessing curricular units credited/enrolled/evaluated/approved as well as their respective grades. Finally, we have unemployment rate, inflation rate and GDP from the region which can help us further understand how economic factors play into student dropout rates or academic success outcomes. This powerful analysis tool will provide valuable insight into what motivates students to stay in school or abandon their studies for a wide range of disciplines such as agronomy, design, education nursing journalism management social service or technologies

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How to use the dataset

This dataset can be used to understand and predict student dropouts and academic outcomes. The data includes a variety of demographic, social-economic and academic performance factors related to the students enrolled in higher education institutions. The dataset provides valuable insights into the factors that affect student success and could be used to guide interventions and policies related to student retention.

Using this dataset, researchers can investigate two key questions: - which specific predictive factors are linked with student dropout or completion? - how do different features interact with each other? For example, researchers could explore if there any demographic characteristics (e.g., gender, age at enrollment etc.) or immersion conditions (e.g., unemployment rate in region) are associated with higher student success rates, as well as understand what implications poverty has for educational outcomes. By answering these questions, research insight is generated which can provide critical information for administrators on formulating strategies that promote successful degree completion among students from diverse backgrounds in their institutions.

In order to use this dataset effectively it is important that scientists familiarize themselves with all variables provided in the dataset including categorical (qualitative) variables such as gender or application mode; numerical variables such as number of curricular units at the beginning of semesters or age at enrollment; ordinal data measurement type variables such as marital status; studied trends over time such as inflation rate or GDP; frequency measurements variables like percentage of scholarship holders; etc.. Additionally scientists should make sure they aware off all potential bias included in the data prior running analysis–for example understanding if one population is underrepresented compared another -as this phenomenon could lead unexpected results if not taken into consideration while conducting research undertaken using this data set.. Finally it would be important for practitioners realize that this current Kaggle Dataset contains only one semester-worth information on each admission intake whereas additional studies conducted for a longer time period might be able provide more accurate results related selected topic area due further deterioration retention achievement coefficients obtained from those gradually accurate experiments unfolding different year-long admissions seasons

Research Ideas

• Prediction of Student Retention: This dataset can be used to develop predictive models that can identify student risk factors for dropout and take early interventions to improve student retention rate.
• Improved Academic Performance: By using this data, higher education institutions could better understand their students' academic progress and identify areas of improvement from both an individual and institutional perspective. This will enable them to develop targeted courses, activities, or initiatives that enhance academic performance more effectively and efficiently.
• Accessibility Assistance: Using the demographic information included in the dataset, institutions could develop s...

Description Ashtead (“Sunbelt”) is the second largest equipment rental company in the States, and cyclical fears plus a few minor operational missteps have created an attractive entry point into a secular winner. I also believe Sunbelt is under-earning to a larger degree than peers because of the organic nature of recent growth. Business Overview I'll keep this short because this and other equipment rental companies have been covered on VIC. Sunbelt buys and maintains a fleet of equipment including aerial work platforms (30% of fleet), forklifts (20%), earthmoving (14%), power and HVAC (11%) and more. Equipment is depreciated over 10 years (chosen to make equipment disposals breakeven at the low point of a cycle) and Sunbelt typically keeps it around for 7 years, getting more than 50% of original cost ("OEC" or original equipment cost) in rental revenue per year. After 7 years, equipment is disposed of at 40 cents on the dollar. Non-resi construction end markets are less than half of the business, and the rest is industrial, MRO and more. Renting equipment lets you get the exact right piece of equipment for a job. As an example, you used to find backhoes on jobsites much more, because a backhoe is the swiss army knife of earthmoving. That user might now prefer to rent either an excavator or a bucket loader, each of which peform half the function of the backhoe but in a more efficient manner. Rental also conserves capital, reduces the need for equipment yards/storage, solves logistics/ eliminates the need for vehicles that can move equipment, and solves the difficulty of maintaining owned equipment. Secular Trends The secular tailwinds come from both increased rental penetration as well as market share gains by the largest players. The use of rented equipment accounts for about 55% of the equipment market today and I expect it to hit at least 65% over time. Penetration is up from the low 40% range pre-GFC and single-digits in the 1990s. The top two players URI and Sunbelt have 15% and 11% share, respectively, and players smaller than the top 100 have 44% of the market. The top 10 players have grown market share from 20% in 2010 to about 45% today. The largest rental company businesses have improved over time. Scale gives purchasing economies with OEM suppliers, efficiencies in logistics and maintenance, and higher equipment utilization. URI and Sunbelt purchase equipment 15-20% cheaper than mom & pop operators. Moving heavy equipment to and from job sites requires a large fleet of dedicated vehicles. Equipment maintenance benefits from having expertise by equipment type, mechanic sharing and better utilization of parts and spares. In a typical branch, 6 out of 20 total employees might be mechanics. Utilization is measured both by time/physical utilization, which is just the amount of time the equipment is on rent, or by dollar utilization, which is measured by the rental revenue divided by the cost of the equipment (basically, asset turns). Dollar utilization is perhaps the most important metric, because it combines the time on rent and the rental rate. Dollar utilization is higher at the scale players for a large variety of reasons. More locations give larger players density and a higher likelihood that a given piece of equipment is needed by someone in that geography. It also lowers transportation costs and time and most importantly allows locations to share equipment. A better repair function means machines are on rent for longer and means that there is more equipment available to rent. A wider variety of equipment on rent also leads to higher rates. Sunbelt frequently mentions that they are not the lowest price, but they win business because of breadth, availability and service. The factors I’ve outlined above have led to stable dollar utilization, rising margins and thus rising returns on capital over time: Specialty rental equipment has become a larger part of Sunbelt’s mix over time. Specialty is a catch-all for equipment that can have more of a service component or more of a temporary, emergency, or one-off use case. When looking at historical results, note that specialty carries lower physical utilization but higher margins. Specialty equipment also depreciates more slowly and is generally less cyclical than general tool (i.e. non-specialty). Cyclical Factors Equipment rental is a cyclical business. Sunbelt will tell you that because equipment rental is now an essential part of customer’s businesses, rather than used as a top-up, future cycles will be more muted than the past. I mostly believe this for a few reasons. First, the large players are larger and more sophisticated. CEO Brendan Horgan likes to say that in the GFC they almost blindly lowered prices by 20% across the board without any pricing tools or great reason to do so. Second, the top 10 players are less leveraged. In the GFC, you not only had more leveraged companies, but some companies actually had covenants tied to time utilization. You can imagine what incentives that creates. Leverage at all the large players has decreased steadily over time. Finally, 70% of the industry contributes and subscribes to Rouse data (owned by RB Global), which provides detailed rate and utilization data by equipment type and geography. This was not the case in the GFC, and even a decade ago large players including Sunbelt did not contribute. Cycles will still happen, but the cash flow characteristics of the business blunt the impact. Rental equipment is typically sold and replaced after seven years, and you can see below that in past downturns capex can effectively be turned off for a time even as aged equipment is still sold. Sunbelt has a young fleet, partly because organic growth necessitates it, and so aging the equipment a year by turning off capex can easily be done in future downturns. Replacing a seventh of your capital every year is actually helpful in downturns because not replacing it means that equilibrium can be reached faster versus having something like a factory running at low levels of utilization. [a note on Sunbelt's fleet age: historically Sunbelt weighted age by net book value, versus gross book value at URI and other US based peers, and this flattered Sunbelt. Sunbelt's fleet is still younger, but this is because they've grown by adding brand new fleet versus M&A/acquiring fleet as URI has done] It’s worth noting that in the Oil & Gas Downturn and during Covid that rates did not fall despite used equipment values falling. Historically this was not the case. Current Conditions The pandemic was characterized by a quick and steep decline in (all) business activity, followed by a scramble to get new equipment. Lead times doubled and tripled and large companies like Sunbelt found themselves waiting almost a year for new equipment, while smaller companies had trouble getting it at all. In recent quarters, equipment availability normalized and Ashtead found itself with slightly more equipment than it would have liked. Inflation is a double edged sword here. Rates need to keep pace with inflation to maintain returns, but Sunbelt and other large players are relatively better off than others. Equipment prices are 20%+ higher than pre-pandemic levels, and Sunbelt has replaced a lot of the fleet at these higher levels, whereas mom & pop players were not able to because of availability. Rates will benefit as these small players replace aged fleet with new fleet at significantly higher prices. Megaprojects, roughly defined as those projects with more than $400mm of value, provide additional opportunities and challenges. The trio of the Jobs Act, the IRA, and the CHIPs act have created a large backlog of megaprojects that will (probably) offset any weakness in commercial construction. Megaprojects favor the larger players. Sunbelt claims 30% market share in these projects, i.e. almost triple their national share. Only the largest players can serve these projects. Sunbelt has examples where they have over $100mm of fleet on a single project. Pandemic related shortages and megaprojects have contributed to recent disappointments in the stock. Both of these factors make it difficult to perfectly plan equipment needs, and ordering equipment early because you’re worried about availability or because you’re staging it for a megaproject can hurt utilization. I view these challenges as easily surmountable. Construction, which is 40% of the customer mix, is rate sensitive, and recently Sunbelt has seen customers delay projects as they wait for clarity on rates. Most of the slack has been taken up by megaprojects ramping. We’re coming off good times, so I think of mid-cycle as normalizing utilization and margin while also accounting for maturing greenfield locations. I think the most likely scenario in the near term is that softness in construction continues to be mostly offset by megaprojects driven by the desire to re-shore and fix our crumbling infrastructure. Valuation As I mentioned earlier, I believe Sunbelt is under-earning. Sunbelt has grown organically to a much larger degree than URI, and they’ve done it by putting new equipment in greenfield locations. These locations take a while to scale from both a fleet and margin perspective. Locations 10+ years old have 56% EBITDA margins. Locations 0-2 years old have 46% margins and locations 2-5 years old have 53% margins. If you apply this to the current store base, mature margins would be three points higher. Margins will also be helped by what Sunbelt calls “cluster economics,” which is just increasing density in markets. Clustered markets carry a few more points of margin and return. I value the business by assuming continued rental penetration, further share gains, and higher returns/margins (note below that I have market share at 13%, but recently an industry publication changed their methodology to include more specialty lines in the market definition and thus share is now

Inflation Rate in China increased to 0.20 percent in October from -0.30 percent in September of 2025. This dataset provides - China Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to Aug 2025 about savings, personal, rate, and USA.

Hollywood Movies Domestic Lifetime Gross and Ranking

An Opportunity for Investigating Box Office Performance

By Elias Dabbas [source]

About this dataset

This dataset contains the details about Hollywood's all-time domestic box office records. It includes data scraped from Box Office Mojo, which breakdowns every movie's lifetime gross, ranking and production year. Domestic gross (adjusted to inflation) has been used as the benchmark to determine what movies were the most successful at the box office in America. This dataset allows you to explore an extensive, comprehensive list of Hollywood all-time biggest hits. Analyze examples of previously unprecedented blockbusters and observe current market trends with this comprehensive overview of domestic box office history - only here at this treasury of motion picture insights!

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This dataset contains comprehensive information about Hollywood movies and their domestic performance at the box office. It includes data on films' production year, lifetime gross, ranking and the studio that produced them. By using this dataset, you can analyze the financial successes and failures of films produced by different studios to gain insights into the Hollywood movie market over time.

The 'rank' column shows each film's ranking compared to other Hollywood movies released in its year of release based on its box office revenue from theaters (not including other sources such as DVD sales or streaming services). The higher the number for a film’s rank means it was more successful financially than other films released in its date window when ticket prices were taken into account; lower numbers equate to less success at that time frame's box office.

The ‘title’ column features all movies analyzed here with links provided which direct users to articles giving background information about those projects - directorial credentials or management history -- as well as full reviews with ratings given by critics while they were screened theatricallly across North America (U.S., Canada).

The ‘studio’ outlines which media conglomerate is credited with distribution/marketing rights for each featured motion picture during their original domestic theatrical runs; these name-brands represent umbrella-corporations comprising multiple divisions specializing in creative development/financing of cinematic works along with doorways engineered around technical know-how -- ie: visual effects shops used by filmmakers during post-production responsibilities their respective productions entailed) -- maintained throughout various industrial regions across entertainment media outlets extending well beyond motion pictures proper... including music/television sector domains defined under respective company flags like Warner Bros., Disney(ABC), NBCUniversal(Comcast) ++ et al mirroring segmentations off any parent brand cited within this database under said label; pertaining solely toward big screen celluloid matters examined herein because charter established assumptions indicate only valid commercially viable feature length fare delivering both titles & collections contained below adheres relevant criterion set forth specifications that warrant inclusion alongside applicable vertical peers made front % center terms established formulating current entries visible within page iteration whilst conforming platform protocols designed enable public

Research Ideas

• Creating a recommendation engine to suggest similar movies based on lifetime gross and year of release.
• Data analysis and visualization of box office trends over time for major Hollywood studios.
• Utilizing the data to recommend alternative ways for movie marketers to invest their advertising budgets in order to maximize their return on investment

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - **Keep i...

Hybrid LCA database generated using ecoinvent3.5 and EXIOBASE3, i.e., each process of the original ecoinvent3.5 database is added new direct inputs (coming from EXIOBASE3) deemed missing (e.g., services). Each process of the resulting hybrid database is thus not (or at least less) truncated and the calculated lifecycle emissions/impacts should therefore be closer to reality.

For license reasons, only the added inputs for each process of ecoinvent are provided (and not all the inputs).

Why are there two versions?

One of the version corresponds to ecoinvent hybridized with the normal version of EXIOBASE and the other is hybridized with a capital-endogenized version of EXIOBASE3.

What does capital endogenization do?

It matches capital goods formation to the value chains of products where they are required. In a more LCA way of speaking, EXIOBASE in its normal version does not allocate capital use to value chains. It's like if ecoinvent processes had no inputs of buildings, etc. in their unit process inventory. For more detail on this, refer to (Södersten et al., 2019) or (Miller et al., 2019).

So which version do I use?

Using the version "with capitals" gives a more comprehensive coverage. Using the "without capitals" version means that if a process of ecoinvent misses inputs of capital goods (e.g., a process does not include the company laptops of the employees), it won't be added. It comes with its fair share of assumptions and uncertainties however.

How do I use the dataset?

First, to use it, you will need ecoinvent3.5 [cut-off] and EXIOBASE3 [product x product, year 2011]. In the two datasets of this package, only added inputs are given (i.e. inputs from EXIOBASE added to ecoinvent processes). Ecoinvent and EXIOBASE processes/sectors are not included, for copyright issues. You thus need both ecoinvent and EXIOBASE to calculate life cycle emissions/impacts.

Module to get ecoinvent in a Python format: https://github.com/majeau-bettez/ecospold2matrix (make sure to take the most up-to-date branch)

Module to get EXIOBASE in a Python format: https://github.com/konstantinstadler/pymrio (can also be installed with pip)

If you want to use the "with capitals" version of the hybrid database, you also need to use the capital endogenized version of EXIOBASE, available here: https://zenodo.org/record/3874309. Choose the pxp version of the year you plan to study (which should match with the year of the EXIOBASE version). You then need to normalize the capital matrix (i.e., divide by the total output x of EXIOBASE). Then, you simply add the normalized capital matrix (K) to the technology matrix (A) of EXIOBASE (see equation below).

Once you have all the data needed, you just need to apply a slightly modified version of the Leontief equation:

\(\begin{equation} \textbf{q}^{hyb} = \begin{bmatrix} \textbf{C}^{lca}\cdot\textbf{S}^{lca} & \textbf{C}^{io}\cdot\textbf{S}^{io} \end{bmatrix} \cdot \left( \textbf{I} - \begin{bmatrix} \textbf{A}^{lca} & \textbf{C}^{d} \\ \textbf{C}^{u} & \textbf{A}^{io}+\textbf{K}^{io} \end{bmatrix} \right) ^{-1} \cdot \left( \begin{bmatrix} \textbf{y}^{lca} \\ 0 \end{bmatrix} \right) \end{equation}\)

q^hyb gives the hybridized impact, i.e., the impacts of each process including the impacts generated by their new inputs.

C^lca and C^io are the respective characterization matrices for ecoinvent and EXIOBASE.

S^lca and S^io are the respective environmental extension matrices (or elementary flows in LCA terms) for ecoinvent and EXIOBASE.

I is the identity matrix.

A^lca and A^io are the respective technology matrices for ecoinvent and EXIOBASE (the ones loaded with ecospold2matrix and pymrio).

K^io is the capital matrix. If you do not use the endogenized version, do not include this matrix in the calculation.

C^u (or upstream cut-offs) is the matrix that you get in this dataset.

C^d (or downstream cut-offs) is simply a matrix of zeros in the case of this application.

Finally you define your final demand (or functional unit/set of functional units for LCA) as y^lca.

Can I use it with different versions of ecoinvent?

No it can only work with ecoinvent3.5. Unfortunately ecoinvent changes the UUIDs of their process between each version and also introduces additional processes.

Can I use it with different versions/reference years of EXIOBASE?

Technically speaking, yes it will work, because the temporal aspect does not intervene in the determination of the hybrid database presented here. However, keep in mind that there might be some inconsistencies. For example, you would need to multiply each of the inputs of the datasets by a factor to account for inflation. Prices of ecoinvent are defined in €2005. The inputs were calculated for the year 2011 using a inflation factor of 1.13.

How do I link UUIDs of ecoinvent to metadata?

Ecospold2matrix stores all the metadata into the PRO matrix.

Why is the equation (I-A)^-1 and not A^-1 like in LCA?

IO and LCA have the same computational background. In LCA however, the convention is to represents outputs and inputs in the technology matrix. That's why there is a diagonal of 1s (the outputs, i.e. functional units) and negative values elsewhere (inputs). In IO, the technology matrix does not include outputs and only registers inputs as positive values. In the end, it is just a convention difference. If we call T the technology matrix of LCA and A the technology matrix of IO we have T = I-A. When you load ecoinvent using ecospold2matrix, the resulting version of ecoinvent will already be in IO convention and you won't have to bother with it.

Pymrio does not provide a characterization matrix for EXIOBASE, what do I do?

You can find an up-to-date characterization matrix (with Impact World+) for environmental extensions of EXIOBASE here: https://zenodo.org/record/3890339

If you want to match characterization across both EXIOBASE and ecoinvent (which you should do), here you can find a characterization matrix with Impact World+ for ecoinvent: https://zenodo.org/record/3890367

It's too complicated...

The custom software that was used to develop these datasets already deals with some of the steps described. Go check it out: https://github.com/MaximeAgez/pylcaio. You can also generate your own hybrid version of ecoinvent3.5 using this software (you can play with some parameters like correction for double counting, inflation rate, change price data to be used, etc.). As of pylcaio v2.1, the resulting hybrid database (generated directly by pylcaio) can be exported and manipulated to brightway2.

Where can I get more information?

The whole methodology is detailed in (Agez et al., 2021).

If needed, I will be happy to help: maxime.agez@polymtl.ca

Inflation Rate in Turkey decreased to 32.87 percent in October from 33.29 percent in September of 2025. This dataset provides the latest reported value for - Turkey Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Monthly and long-term Portugal economic indicators data: historical series and analyst forecasts curated by FocusEconomics.

View data of PCE, an index that measures monthly changes in the price of consumer goods and services as a means of analyzing inflation.

This dataset provides information about 277 UK learned societies that published peer reviewed journals in 2015, illustrating how the nature of their publishing activities had changed by 2023. The dataset includes information such as outsourced publishing partners, number of journals published (1, 2 or 3+), incoming resources, publishing revenues and publishing models.

Learned society publishers represent a critical part of the publishing and scholarly communications ecosystem and the impact of changes in the landscape on this group of stakeholders as a whole is not well studied or understood. This dataset provides important insights into how learned society publishing in the UK has changed over time, showing that the number of self-published societies has reduced by 35% since 2015, that outsourcing relationships have become more complex and that societies' revenues from publishing have, in the main, failed to keep pace with inflation.

If you have any questions or comments, or wish to propose amendments to the information included in the dataset, please contact Rob Johnson at rob.johnson@research-consulting.com.

Inflation Rate In the Euro Area increased to 2.20 percent in November from 2.10 percent in October of 2025. This dataset provides the latest reported value for - Euro Area Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.