Fundamental Data Science Large Value ETF Investment Strategy - Fundamental Data Science Large Value ETF	Jun. 30, 2026
Prospectus [Line Items]
Strategy [Heading]	<span style="color:#000000;font-family:Arial Narrow;font-size:11pt;font-weight:bold;">What are the Fund’s main investment strategies?</span>
Strategy Narrative [Text Block]	Under normal circumstances, the Fund invests at least 80% of its Assets in equity securities of large, well established value companies.Large, well established companies are companies with market capitalizations similar to those within the universe of the Russell 1000® Value Index, at the time of purchase. As of September 30, 2026, the market capitalizations of the companies in the Russell 1000® Value Index ranged from $1.76 billion to $4.81 trillion.Value companies are those that the adviser believes are undervalued or attractively valued, based on one or more of the following factors: how the stock price compares to the company’s sales, earnings, and assets, as well as the overall value of the company compared to its sales and the capital invested in it.“Assets” means net assets, plus the amount of borrowings for investment purposes. In implementing its main strategies, the Fund invests primarily in common stocks. The Fund may also invest in real estate investment trusts (REITs). In addition to direct investments in securities, derivatives, which are instruments that have a value based on another instrument, exchange rate or index, may be used as substitutes for securities in which the Fund can invest. The Fund may use futures contracts, options, swaps and forward contracts as tools in the management of portfolio assets. The Fund may use derivatives to hedge various investments, for risk management and/or to increase income or gain to the Fund. Investment Process: In managing the Fund, the adviser employs a fundamental data science enabled investment approach that combines research, data insights, and risk management. The adviser defines data science as the discipline of extracting useful insights from collections of information, and the adviser utilizes the insights as part of its investment process. The adviser also utilizes proprietary techniques to process, analyze, and combine a wide variety of data sources, including the adviser’s multi-decade history of proprietary fundamental research, company financial statements, and a variety of other data sources that the adviser finds relevant to forecasting the financial prospects of each security, also known as fundamental analysis. The adviser combines insights derived from these sources to conduct fundamental analysis. Alongside its own insights, the Fund’s portfolio management team uses the forecasts developed through data science techniques to help to identify securities with attractive valuations that are priced favorably relative to their associated levels of risk. The Fund’s portfolio management team then constructs a portfolio that seeks to maximize expected future financial performance while controlling for key risks to the underlying companies’ businesses identified by the adviser’s analysis. The adviser assesses key risks by analyzing potential events or conditions that may have a negative impact on the adviser’s valuation of a particular security. Such key risks may include, but are not limited to, sensitivity to changes in macroeconomic conditions, competitive risks from existing companies or new entrants, and operational risks related to the companies’ business models. The adviser regularly evaluates the efficacy of the sources of information included within the investment process, and seeks to identify new data sources that will be additive to the adviser’s forecasts and portfolio construction, assessing the validity of its models and assumptions as new information becomes available and market conditions change. As part of its investment process, the adviser seeks to assess the impact of environmental, social and governance (ESG) factors on many issuers in the universe in which the Fund may invest. The adviser’s assessment is based on an analysis of key opportunities and risks across industries to seek to identify financially material issues with respect to the Fund’s investments in issuers and ascertain key issues that merit engagement with issuers. These assessments may not be conclusive, and securities of issuers that may be negatively impacted by such factors may be purchased and retained by the Fund while the Fund may divest or not invest in securities of issuers that may be positively impacted by such factors. The adviser may sell a security for several reasons. A security may be sold due to a change in the company’s fundamentals or if the adviser believes the security is no longer attractively valued relative to its associated levels of risk. Investments may also be sold if the adviser identifies a stock that it believes offers a better investment opportunity.

Fundamental Data Science Large Value ETF Investment Strategy - Fundamental Data Science Large Value ETF

Jun. 30, 2026

<span style="color:#000000;font-family:Arial Narrow;font-size:11pt;font-weight:bold;">What are the Fund’s main investment strategies?</span>

Strategy Narrative [Text Block]

Under normal circumstances, the Fund invests at least 80% of its Assets in equity securities of large, well established value companies.Large, well established companies are companies with market capitalizations similar to those within the universe of the Russell 1000® Value Index, at the time of purchase. As of September 30, 2026, the market capitalizations of the companies in the Russell 1000® Value Index ranged from $1.76 billion to $4.81 trillion.Value companies are those that the adviser believes are undervalued or attractively valued, based on one or more of the following factors: how the stock price compares to the company’s sales, earnings, and assets, as well as the overall value of the company compared to its sales and the capital invested in it.“Assets” means net assets, plus the amount of borrowings for investment purposes. In implementing its main strategies, the Fund invests primarily in common stocks. The Fund may also invest in real estate investment trusts (REITs). In addition to direct investments in securities, derivatives, which are instruments that have a value based on another instrument, exchange rate or index, may be used as substitutes for securities in which the Fund can invest. The Fund may use futures contracts, options, swaps and forward contracts as tools in the management of portfolio assets. The Fund may use derivatives to hedge various investments, for risk management and/or to increase income or gain to the Fund. Investment Process: In managing the Fund, the adviser employs a fundamental data science enabled investment approach that combines research, data insights, and risk management. The adviser defines data science as the discipline of extracting useful insights from collections of information, and the adviser utilizes the insights as part of its investment process. The adviser also utilizes proprietary techniques to process, analyze, and combine a wide variety of data sources, including the adviser’s multi-decade history of proprietary fundamental research, company financial statements, and a variety of other data sources that the adviser finds relevant to forecasting the financial prospects of each security, also known as fundamental analysis. The adviser combines insights derived from these sources to conduct fundamental analysis. Alongside its own insights, the Fund’s portfolio management team uses the forecasts developed through data science techniques to help to identify securities with attractive valuations that are priced favorably relative to their associated levels of risk. The Fund’s portfolio management team then constructs a portfolio that seeks to maximize expected future financial performance while controlling for key risks to the underlying companies’ businesses identified by the adviser’s analysis. The adviser assesses key risks by analyzing potential events or conditions that may have a negative impact on the adviser’s valuation of a particular security. Such key risks may include, but are not limited to, sensitivity to changes in macroeconomic conditions, competitive risks from existing companies or new entrants, and operational risks related to the companies’ business models. The adviser regularly evaluates the efficacy of the sources of information included within the investment process, and seeks to identify new data sources that will be additive to the adviser’s forecasts and portfolio construction, assessing the validity of its models and assumptions as new information becomes available and market conditions change. As part of its investment process, the adviser seeks to assess the impact of environmental, social and governance (ESG) factors on many issuers in the universe in which the Fund may invest. The adviser’s assessment is based on an analysis of key opportunities and risks across industries to seek to identify financially material issues with respect to the Fund’s investments in issuers and ascertain key issues that merit engagement with issuers. These assessments may not be conclusive, and securities of issuers that may be negatively impacted by such factors may be purchased and retained by the Fund while the Fund may divest or not invest in securities of issuers that may be positively impacted by such factors. The adviser may sell a security for several reasons. A security may be sold due to a change in the company’s fundamentals or if the adviser believes the security is no longer attractively valued relative to its associated levels of risk. Investments may also be sold if the adviser identifies a stock that it believes offers a better investment opportunity.